https://github.com/xcfcode/Summarization-Papers

Summarization Papers
https://github.com/xcfcode/Summarization-Papers
chatgpt natural-language-processing nlp pretrained-language-model summarization text-generation
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Summarization Papers
Host: GitHub
URL: https://github.com/xcfcode/Summarization-Papers
Owner: xcfcode
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Created: 2020-10-14T15:48:29.000Z (over 5 years ago)
Default Branch: main
Last Pushed: 2023-07-15T09:34:29.000Z (almost 3 years ago)
Last Synced: 2024-08-03T21:04:41.872Z (almost 2 years ago)
Topics: chatgpt, natural-language-processing, nlp, pretrained-language-model, summarization, text-generation
Language: TeX
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- Readme: README.md
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README

          


 Summarization Papers




 Full List



Organized by [Xiachong Feng](http://xcfeng.net/).

## Contributor

[Yichong Huang](https://github.com/OrangeInSouth), [Haozheng Yang](https://github.com/hzyang95), [Jiaan Wang](https://github.com/krystalan)

## Summarization Learning Route

[Summarization Learning Route (with link)](http://xcfeng.net/res/summarization-route.pdf)

![](./pic/route.png)

## Trending

![](./pic/trending.png)

## Presentations && Notes

* [Dialogue Summarization (2022.1)](slides/presentation/Dialogue_Summarization_DAMO.pdf) ![](https://img.shields.io/badge/-presentations-brightgreen)

* [Cross-lingual Summarization](slides/presentation/Cross-lingual_Summarization.pdf) ![](https://img.shields.io/badge/-presentations-brightgreen) 

* [如何把DialoGPT用到对话摘要任务？@ ACL 2021](https://mp.weixin.qq.com/s/GQQRRS3F7p4Zv6wSuDh0ng) ![](https://img.shields.io/badge/-blog-red) 

* [对话摘要最新进展简述](https://mp.weixin.qq.com/s/628OAOW1_-Yc_vQbeuY_uA) ![](https://img.shields.io/badge/-blog-red) 

* [Dialogue Summarization (2021.5)](slides/presentation/Dialogue_Summarization.pdf) ![](https://img.shields.io/badge/-presentations-brightgreen) 

* [融入常识知识的生成式对话摘要](https://mp.weixin.qq.com/s/x3zqGc4pqh4x3q_uorNKcg) ![](https://img.shields.io/badge/-blog-red) 

* [会议摘要有难度？快来引入对话篇章结构信息](https://mp.weixin.qq.com/s/Be7AYUPdux8NvAO4wo6_fg) ![](https://img.shields.io/badge/-blog-red) 

* [文本摘要论文列表(Chinese)](https://mp.weixin.qq.com/s/tLdLGSFl229selxeogQk-w) ![](https://img.shields.io/badge/-blog-red) 

* [事实感知的生成式文本摘要(Chinese)](https://mp.weixin.qq.com/s/Aye9FBwG-v2JO2MLoEjo0g) ![](https://img.shields.io/badge/-blog-red) 

* [多模态摘要简述(Chinese)](https://mp.weixin.qq.com/s/Ce6jtp-gTtqeh9lgi-kHtQ) ![](https://img.shields.io/badge/-blog-red) 

* [文本摘要简述](https://mp.weixin.qq.com/s/NGpDrYilAeuH6pQji0ujaA) ![](https://img.shields.io/badge/-blog-red) 

* [Multi-modal Summarization](slides/presentation/Multi-modal-Summarization.pdf) ![](https://img.shields.io/badge/-presentations-brightgreen) 

* [ACL20 Summarization](slides/presentation/acl2020-summarization.pdf) ![](https://img.shields.io/badge/-presentations-brightgreen) 

* [文本摘要简述 (Chinese)](slides/presentation/文本摘要简述.pdf) ![](https://img.shields.io/badge/-presentations-brightgreen) 

* [ACL19 Summarization](slides/presentation/ACL19%20Summarization.pdf) ![](https://img.shields.io/badge/-presentations-brightgreen) 

* [Brief intro to summarization (Chinese)](slides/notes/Brief-intro-to-summarization.pdf) ![](https://img.shields.io/badge/-notes-orange)

* [EMNLP19 Summarization (Chinese)](slides/notes/EMNLP19_Summarization.pdf) ![](https://img.shields.io/badge/-notes-orange)

* [ACL19-A Simple Theoretical Model of Importance for Summarization](slides/paper-slides/A%20Simple%20Theoretical%20Model%20of%20Importance%20for%20Summarization.pdf) ![](https://img.shields.io/badge/-papers-blue)

* [ACL19-Multimodal Abstractive Summarization for How2 Videos](slides/paper-slides/Multimodal%20Abstractive%20Summarization%20for%20How2%20Videos.pdf) ![](https://img.shields.io/badge/-papers-blue)

## Big Model Era

1. **TempoSum: Evaluating the Temporal Generalization of Abstractive Summarization** *Chi Seng Cheang, Hou Pong Chan, Derek F. Wong, Xuebo Liu, Zhaocong Li, Yanming Sun, Shudong Liu, Lidia S. Chao* `` [[pdf]](https://arxiv.org/abs/2305.01951) [[data]](https://github.com/AndyCheang/TempoSum)  [Abs] Recent pre-trained language models (PLMs) achieve promising results in existing abstractive summarization datasets. However, existing summarization benchmarks overlap in time with the standard pre-training corpora and finetuning datasets. Hence, the strong performance of PLMs may rely on the parametric knowledge that is memorized during pre-training and fine-tuning. Moreover, the knowledge memorized by PLMs may quickly become outdated, which affects the generalization performance of PLMs on future data. In this work, we propose TempoSum, a novel benchmark that contains data samples from 2010 to 2022, to understand the temporal generalization ability of abstractive summarization models. Through extensive human evaluation, we show that parametric knowledge stored in summarization models significantly affects the faithfulness of the generated summaries on future data. Moreover, existing faithfulness enhancement methods cannot reliably improve the faithfulness of summarization models on future data. Finally, we discuss several recommendations to the research community on how to evaluate and improve the temporal generalization capability of text summarization models. 

1. **RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models** *Dave Van Veen, Cara Van Uden, Maayane Attias, Anuj Pareek, Christian Bluethgen, Malgorzata Polacin, Wah Chiu, Jean-Benoit Delbrouck, Juan Manuel Zambrano Chaves, Curtis P. Langlotz, Akshay S. Chaudhari, John Pauly* [[pdf]](https://arxiv.org/abs/2305.01146)  [Abs] We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical text, and clinical text) and via prompting (zero-shot, in-context learning) or parameter-efficient fine-tuning (prefix tuning, LoRA). Our results on the MIMIC-III dataset consistently demonstrate best performance by maximally adapting to the task via pretraining on clinical text and parameter-efficient fine-tuning on RRS examples. Importantly, this method fine-tunes a mere 0.32% of parameters throughout the model, in contrast to end-to-end fine-tuning (100% of parameters). Additionally, we study the effect of in-context examples and out-of-distribution (OOD) training before concluding with a radiologist reader study and qualitative analysis. Our findings highlight the importance of domain adaptation in RRS and provide valuable insights toward developing effective natural language processing solutions for clinical tasks. 

1. **ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT** *Chong Ma, Zihao Wu, Jiaqi Wang, Shaochen Xu, Yaonai Wei, Zhengliang Liu, Lei Guo, Xiaoyan Cai, Shu Zhang, Tuo Zhang, Dajiang Zhu, Dinggang Shen, Tianming Liu, Xiang Li* [[pdf]](https://arxiv.org/abs/2304.08448)    [Abs] The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section. However, writing numerous impressions can be laborious and error-prone for radiologists. Although recent studies have achieved promising results in automatic impression generation using large-scale medical text data for pre-training and fine-tuning pre-trained language models, such models often require substantial amounts of medical text data and have poor generalization performance. While large language models (LLMs) like ChatGPT have shown strong generalization capabilities and performance, their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, which leverages the in-context learning capability of LLMs by constructing dynamic contexts using domain-specific, individualized data. This dynamic prompt approach enables the model to learn contextual knowledge from semantically similar examples from existing data. Additionally, we design an iterative optimization algorithm that performs automatic evaluation on the generated impression results and composes the corresponding instruction prompts to further optimize the model. The proposed ImpressionGPT model achieves state-of-the-art performance on both MIMIC-CXR and OpenI datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various domains. 

1. **Extractive Summarization via ChatGPT for Faithful Summary Generation** *Haopeng Zhang, Xiao Liu, Jiawei Zhang* [[pdf]](https://arxiv.org/abs/2304.04193)   [Abs] Extractive summarization is a crucial task in natural language processing that aims to condense long documents into shorter versions by directly extracting sentences. The recent introduction of ChatGPT has attracted significant interest in the NLP community due to its remarkable performance on a wide range of downstream tasks. However, concerns regarding factuality and faithfulness have hindered its practical applications for summarization systems. This paper first presents a thorough evaluation of ChatGPT's performance on extractive summarization and compares it with traditional fine-tuning methods on various benchmark datasets. Our experimental analysis reveals that ChatGPT's extractive summarization performance is still inferior to existing supervised systems in terms of ROUGE scores. In addition, we explore the effectiveness of in-context learning and chain-of-thought reasoning for enhancing its performance. Furthermore, we find that applying an extract-then-generate pipeline with ChatGPT yields significant performance improvements over abstractive baselines in terms of summary faithfulness. These observations highlight potential directions for enhancing ChatGPT's capabilities for faithful text summarization tasks using two-stage approaches. 

1. **Human-like Summarization Evaluation with ChatGPT** *Mingqi Gao, Jie Ruan, Renliang Sun, Xunjian Yin, Shiping Yang, Xiaojun Wan* [[pdf]](https://arxiv.org/abs/2304.02554)  [Abs] Evaluating text summarization is a challenging problem, and existing evaluation metrics are far from satisfactory. In this study, we explored ChatGPT's ability to perform human-like summarization evaluation using four human evaluation methods on five datasets. We found that ChatGPT was able to complete annotations relatively smoothly using Likert scale scoring, pairwise comparison, Pyramid, and binary factuality evaluation. Additionally, it outperformed commonly used automatic evaluation metrics on some datasets. Furthermore, we discussed the impact of different prompts, compared its performance with that of human evaluation, and analyzed the generated explanations and invalid responses. 

1. **Cross-Lingual Summarization via ChatGPT** *Jiaan Wang, Yunlong Liang, Fandong Meng, Zhixu Li, Jianfeng Qu, Jie Zhou* [[pdf]](https://arxiv.org/abs/2302.14229)   [Abs] Given a document in a source language, cross-lingual summarization (CLS) aims to generate a summary in a different target language. Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. However, it is not yet known the performance of ChatGPT on CLS. In this report, we empirically use various prompts to guide ChatGPT to perform zero-shot CLS from different paradigms (i.e., end-to-end and pipeline), and provide a preliminary evaluation on its generated summaries.We find that ChatGPT originally prefers to produce lengthy summaries with more detailed information. But with the help of an interactive prompt, ChatGPT can balance between informativeness and conciseness, and significantly improve its CLS performance. Experimental results on three widely-used CLS datasets show that ChatGPT outperforms the advanced GPT 3.5 model (i.e., text-davinci-003). In addition, we provide qualitative case studies to show the superiority of ChatGPT on CLS. 

1. **Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization** *Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, Wei Cheng* [[pdf]](https://arxiv.org/abs/2302.08081)  [Abs] Text summarization has been a crucial problem in natural language processing (NLP) for several decades. It aims to condense lengthy documents into shorter versions while retaining the most critical information. Various methods have been proposed for text summarization, including extractive and abstractive summarization. The emergence of large language models (LLMs) like GPT3 and ChatGPT has recently created significant interest in using these models for text summarization tasks. Recent studies \cite{goyal2022news, zhang2023benchmarking} have shown that LLMs-generated news summaries are already on par with humans. However, the performance of LLMs for more practical applications like aspect or query-based summaries is underexplored. To fill this gap, we conducted an evaluation of ChatGPT's performance on four widely used benchmark datasets, encompassing diverse summaries from Reddit posts, news articles, dialogue meetings, and stories. Our experiments reveal that ChatGPT's performance is comparable to traditional fine-tuning methods in terms of Rouge scores. Moreover, we highlight some unique differences between ChatGPT-generated summaries and human references, providing valuable insights into the superpower of ChatGPT for diverse text summarization tasks. Our findings call for new directions in this area, and we plan to conduct further research to systematically examine the characteristics of ChatGPT-generated summaries through extensive human evaluation. 

2. **News Summarization and Evaluation in the Era of GPT-3** *Tanya Goyal, Junyi Jessy Li, Greg Durrett* [[pdf]](https://arxiv.org/abs/2209.12356) [[code]](https://tagoyal.github.io/zeroshot-news-annotations.html)  [Abs] The recent success of zero- and few-shot prompting with models like GPT-3 has led to a paradigm shift in NLP research. In this paper, we study its impact on text summarization, focusing on the classic benchmark domain of news summarization. First, we investigate how zero-shot GPT-3 compares against fine-tuned models trained on large summarization datasets. We show that not only do humans overwhelmingly prefer GPT-3 summaries, but these also do not suffer from common dataset-specific issues such as poor factuality. Next, we study what this means for evaluation, particularly the role of gold standard test sets. Our experiments show that both reference-based and reference-free automatic metrics, e.g. recently proposed QA- or entailment-based factuality approaches, cannot reliably evaluate zero-shot summaries. Finally, we discuss future research challenges beyond generic summarization, specifically, keyword- and aspect-based summarization, showing how dominant fine-tuning approaches compare to zero-shot prompting. To support further research, we release: (a) a corpus of 10K generated summaries from fine-tuned and zero-shot models across 4 standard summarization benchmarks, (b) 1K human preference judgments and rationales comparing different systems for generic- and keyword-based summarization. 

3. **Benchmarking Large Language Models for News Summarization** *Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto* [[pdf]](https://arxiv.org/abs/2301.13848)  [Abs] Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model scales, we make two important observations. First, we find instruction tuning, and not model size, is the key to the LLM's zero-shot summarization capability. Second, existing studies have been limited by low-quality references, leading to underestimates of human performance and lower few-shot and finetuning performance. To better evaluate LLMs, we perform human evaluation over high-quality summaries we collect from freelance writers. Despite major stylistic differences such as the amount of paraphrasing, we find that LMM summaries are judged to be on par with human written summaries. 

4. **Is ChatGPT a General-Purpose Natural Language Processing Task Solver?** *Chengwei Qin, Aston Zhang, Zhuosheng Zhang, Jiaao Chen, Michihiro Yasunaga, Diyi Yang* [[pdf]](https://arxiv.org/abs/2302.06476)  [Abs] Spurred by advancements in scale, large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot -- i.e., without adaptation on downstream data. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community due to the fact that it can generate high-quality responses to human input and self-correct previous mistakes based on subsequent conversations. However, it is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot. In this work, we empirically analyze the zero-shot learning ability of ChatGPT by evaluating it on 20 popular NLP datasets covering 7 representative task categories. With extensive empirical studies, we demonstrate both the effectiveness and limitations of the current version of ChatGPT. We find that ChatGPT performs well on many tasks favoring reasoning capabilities (e.g., arithmetic reasoning) while it still faces challenges when solving specific tasks such as sequence tagging. We additionally provide in-depth analysis through qualitative case studies. 

5. **A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity** *Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, Pascale Fung* `` [[pdf]](https://arxiv.org/abs/2302.04023)  [Abs] This paper proposes a framework for quantitatively evaluating interactive LLMs such as ChatGPT using publicly available data sets. We carry out an extensive technical evaluation of ChatGPT using 21 data sets covering 8 different common NLP application tasks. We evaluate the multitask, multilingual and multi-modal aspects of ChatGPT based on these data sets and a newly designed multimodal dataset. We find that ChatGPT outperforms LLMs with zero-shot learning on most tasks and even outperforms fine-tuned models on some tasks. We find that it is better at understanding non-Latin script languages than generating them. It is able to generate multimodal content from textual prompts, via an intermediate code generation step. Moreover, we find that ChatGPT is 64.33% accurate on average in 10 different reasoning categories under logical reasoning, non-textual reasoning, and commonsense reasoning, hence making it an unreliable reasoner. It is, for example, better at deductive than inductive reasoning. ChatGPT suffers from hallucination problems like other LLMs and it generates more extrinsic hallucinations from its parametric memory as it does not have access to an external knowledge base. Finally, the interactive feature of ChatGPT enables human collaboration with the underlying LLM to improve its performance, i.e, 8% ROUGE-1 on summarization and 2% ChrF++ on machine translation, in a multi-turn "prompt engineering" fashion. 

## Decomposed

1. **Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees** *Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal* `ICLR 2023` [[pdf]](https://arxiv.org/abs/2209.10492) [[code]](https://github.com/swarnaHub/SummarizationPrograms)     [Abs] Current abstractive summarization models either suffer from a lack of clear interpretability or provide incomplete rationales by only highlighting parts of the source document. To this end, we propose the Summarization Program (SP), an interpretable modular framework consisting of an (ordered) list of binary trees, each encoding the step-by-step generative process of an abstractive summary sentence from the source document. A Summarization Program contains one root node per summary sentence, and a distinct tree connects each summary sentence (root node) to the document sentences (leaf nodes) from which it is derived, with the connecting nodes containing intermediate generated sentences. Edges represent different modular operations involved in summarization such as sentence fusion, compression, and paraphrasing. We first propose an efficient best-first search method over neural modules, SP-Search that identifies SPs for human summaries by directly optimizing for ROUGE scores. Next, using these programs as automatic supervision, we propose seq2seq models that generate Summarization Programs, which are then executed to obtain final summaries. We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior. We also conduct a simulation study to show that Summarization Programs improve the interpretability of summarization models by allowing humans to better simulate model reasoning. Summarization Programs constitute a promising step toward interpretable and modular abstractive summarization, a complex task previously addressed primarily through blackbox end-to-end neural systems. Supporting code available at this https URL 

## Benchmark

* **Benchmarking Large Language Models for News Summarizatio** *Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto* [[pdf]](https://arxiv.org/abs/2301.13848)    [Abs] Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood. By conducting a human evaluation on ten LLMs across different pretraining methods, prompts, and model scales, we make two important observations. First, we find instruction tuning, and not model size, is the key to the LLM's zero-shot summarization capability. Second, existing studies have been limited by low-quality references, leading to underestimates of human performance and lower few-shot and finetuning performance. To better evaluate LLMs, we perform human evaluation over high-quality summaries we collect from freelance writers. Despite major stylistic differences such as the amount of paraphrasing, we find that LMM summaries are judged to be on par with human written summaries. 

* **MuLD: The Multitask Long Document Benchmark** *G Thomas Hudson, Noura Al Moubayed* [[pdf]](https://arxiv.org/abs/2202.07362) [[data]](https://github.com/ghomasHudson/muld)

* **EXPLAINABOARD: An Explainable Leaderboard for NLP** *Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaichen Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, Graham Neubig* [[pdf]](http://explainaboard.nlpedia.ai/ExplainaBoard.pdf) [[ExplainaBoard]](http://explainaboard.nlpedia.ai/leaderboard/task-summ/index.php)

* **GLGE: A New General Language Generation Evaluation Benchmark** *Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan* [[pdf]](https://arxiv.org/abs/2011.11928) [[benchmark]](https://github.com/microsoft/glge)

## Survey

1. **A Survey on Biomedical Text Summarization with Pre-trained Language Model** *Qianqian Xie, Zheheng Luo, Benyou Wang, Sophia Ananiadou* [[pdf]](https://arxiv.org/abs/2304.08763)  [[code]](https://github.com/zhehengluoK/Biomedical-Text-Summarization-Survey/tree/master)  [Abs] The exponential growth of biomedical texts such as biomedical literature and electronic health records (EHRs), provides a big challenge for clinicians and researchers to access clinical information efficiently. To address the problem, biomedical text summarization has been proposed to support clinical information retrieval and management, aiming at generating concise summaries that distill key information from single or multiple biomedical documents. In recent years, pre-trained language models (PLMs) have been the de facto standard of various natural language processing tasks in the general domain. Most recently, PLMs have been further investigated in the biomedical field and brought new insights into the biomedical text summarization task. In this paper, we systematically summarize recent advances that explore PLMs for biomedical text summarization, to help understand recent progress, challenges, and future directions. We categorize PLMs-based approaches according to how they utilize PLMs and what PLMs they use. We then review available datasets, recent approaches and evaluation metrics of the task. We finally discuss existing challenges and promising future directions. To facilitate the research community, we line up open resources including available datasets, recent approaches, codes, evaluation metrics, and the leaderboard in a public project: this https URL. 

1. **A Survey on Medical Document Summarization** *Raghav Jain, Anubhav Jangra, Sriparna Saha, Adam Jatowt*  [[pdf]](https://arxiv.org/abs/2212.01669)   [Abs] The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and patients alike, summarizing it has become increasingly necessary - this has been supported through the introduction of deep learning and transformer-based networks, which have boosted the sector significantly in recent years. This paper gives a comprehensive survey of the current techniques and trends in medical summarization 

2. **Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions** *Qi Jia, Siyu Ren, Yizhu Liu, Kenny Q. Zhu* [[pdf]](https://arxiv.org/abs/2210.09894)  [Abs] Abstractive dialogue summarization is to generate a concise and fluent summary covering the salient information in a dialogue among two or more interlocutors. It has attracted great attention in recent years based on the massive emergence of social communication platforms and an urgent requirement for efficient dialogue information understanding and digestion. Different from news or articles in traditional document summarization, dialogues bring unique characteristics and additional challenges, including different language styles and formats, scattered information, flexible discourse structures and unclear topic boundaries. This survey provides a comprehensive investigation on existing work for abstractive dialogue summarization from scenarios, approaches to evaluations. It categorizes the task into two broad categories according to the type of input dialogues, i.e., open-domain and task-oriented, and presents a taxonomy of existing techniques in three directions, namely, injecting dialogue features, designing auxiliary training tasks and using additional data.A list of datasets under different scenarios and widely-accepted evaluation metrics are summarized for completeness. After that, the trends of scenarios and techniques are summarized, together with deep insights on correlations between extensively exploited features and different scenarios. Based on these analyses, we recommend future directions including more controlled and complicated scenarios, technical innovations and comparisons, publicly available datasets in special domains, etc. 

3. **A Survey of Automatic Text Summarization Using Graph Neural Networks** *Marco Ferdinand Salchner, Adam Jatowt* `COLING 2022` [[pdf]](https://aclanthology.org/2022.coling-1.536/)  [Abs] Although automatic text summarization (ATS) has been researched for several decades, the application of graph neural networks (GNNs) to this task started relatively recently. In this survey we provide an overview on the rapidly evolving approach of using GNNs for the task of automatic text summarization. In particular we provide detailed information on the functionality of GNNs in the context of ATS, and a comprehensive overview of models utilizing this approach.  

4. **A Survey on Cross-Lingual Summarization** *Jiaan Wang, Fandong Meng, Duo Zheng, Yunlong Liang, Zhixu Li, Jianfeng Qu, Jie Zhou* `TACL 2022` [[pdf]](https://arxiv.org/abs/2203.12515)  [Abs] Cross-lingual summarization is the task of generating a summary in one language (e.g., English) for the given document(s) in a different language (e.g., Chinese). Under the globalization background, this task has attracted increasing attention of the computational linguistics community. Nevertheless, there still remains a lack of comprehensive review for this task. Therefore, we present the first systematic critical review on the datasets, approaches, and challenges in this field. Specifically, we carefully organize existing datasets and approaches according to different construction methods and solution paradigms, respectively. For each type of datasets or approaches, we thoroughly introduce and summarize previous efforts and further compare them with each other to provide deeper analyses. In the end, we also discuss promising directions and offer our thoughts to facilitate future research. This survey is for both beginners and experts in cross-lingual summarization, and we hope it will serve as a starting point as well as a source of new ideas for researchers and engineers interested in this area. 

5. **An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics** *uan Yee Koh, Jiaxin Ju, Ming Liu, Shirui Pan* `ACM Computing Surveys` [[pdf]](https://arxiv.org/abs/2207.00939)  [Abs] Long documents such as academic articles and business reports have been the standard format to detail out important issues and complicated subjects that require extra attention. An automatic summarization system that can effectively condense long documents into short and concise texts to encapsulate the most important information would thus be significant in aiding the reader's comprehension. Recently, with the advent of neural architectures, significant research efforts have been made to advance automatic text summarization systems, and numerous studies on the challenges of extending these systems to the long document domain have emerged. In this survey, we provide a comprehensive overview of the research on long document summarization and a systematic evaluation across the three principal components of its research setting: benchmark datasets, summarization models, and evaluation metrics. For each component, we organize the literature within the context of long document summarization and conduct an empirical analysis to broaden the perspective on current research progress. The empirical analysis includes a study on the intrinsic characteristics of benchmark datasets, a multi-dimensional analysis of summarization models, and a review of the summarization evaluation metrics. Based on the overall findings, we conclude by proposing possible directions for future exploration in this rapidly growing field. 

6. **Multi-document Summarization via Deep Learning Techniques: A Survey** *Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, QUAN Z. Sheng* [[pdf]](https://dl.acm.org/doi/10.1145/3529754)

7. **Embedding Knowledge for Document Summarization: A Survey** *Yutong Qu, Wei Emma Zhang, Jian Yang, Lingfei Wu, Jia Wu, Xindong Wu* [[pdf]](https://arxiv.org/abs/2204.11190)

8. **A Survey on Dialogue Summarization: Recent Advances and New Frontiers** *Xiachong Feng, Xiaocheng Feng, Bing Qin* `IJCAI 2022, Survey Track` [[pdf]](https://arxiv.org/abs/2107.03175)

9. **Automatic Text Summarization Methods: A Comprehensive Review** *Divakar Yadav, Jalpa Desai, Arun Kumar Yadav* [[pdf]](https://arxiv.org/abs/2204.01849)

10. **Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods** *Wei Li, Wenhao Wu, Moye Chen, Jiachen Liu, Xinyan Xiao, Hua Wu* [[pdf]](https://arxiv.org/abs/2203.05227)

11. **Recent Advances in Neural Text Generation: A Task-Agnostic Survey** *Chen Tang, Frank Guerin, Yucheng Li, Chenghua Lin* [[pdf]](https://arxiv.org/abs/2203.03047)

12. **Survey of Hallucination in Natural Language Generation** *Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, Pascale Fung* [[pdf]](https://arxiv.org/abs/2202.03629)

13. **A Survey on Retrieval-Augmented Text Generation** *Huayang Li, Yixuan Su, Deng Cai, Yan Wang, Lemao Liu* [[pdf]](https://arxiv.org/abs/2202.01110)

14. **A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models** *Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song* [[pdf]](https://arxiv.org/abs/2201.05337)

15. **A Survey of Pretrained Language Models Based Text Generation** *Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong Wen* [[pdf]](https://arxiv.org/abs/2201.05273)

16. **A Comprehensive Review on Summarizing Financial News Using Deep Learning** *Saurabh Kamal, Sahil Sharma* [[pdf]](https://arxiv.org/abs/2109.10118)

17. **A Survey on Multi-modal Summarization** *Anubhav Jangra, Adam Jatowt, Sriparna Saha, Mohammad Hasanuzzaman* [[pdf]](https://arxiv.org/abs/2109.05199)

18. **Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing** *Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig* [[pdf]](https://arxiv.org/abs/2107.13586)

19. **Pretrained Language Models for Text Generation: A Survey** *Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen* `IJCAI21` [[pdf]](https://arxiv.org/abs/2105.10311)

20. **A Survey of Recent Abstract Summarization Techniques** *Diyah Puspitaningrum* `ICICT21` [[pdf]](https://arxiv.org/abs/2105.00824)

21. **A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization** *AYESHA AYUB SYED, FORD LUMBAN GAOL, TOKURO MATSUO* [[pdf]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9328413)

22. **Automatic summarization of scientific articles: A survey** *Nouf Ibrahim Altmami, Mohamed El Bachir Menai* `Journal of King Saud University - Computer and Information Sciences` [[pdf]](https://www.sciencedirect.com/science/article/pii/S1319157820303554)

23. **Multi-document Summarization via Deep Learning Techniques: A Survey** *Congbo Ma, Wei Emma Zhang, Mingyu Guo, Hu Wang, Quan Z. Sheng* [[pdf]](https://arxiv.org/abs/2011.04843) 

24. **Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges** *Dima Suleiman, Arafat A. Awajan* [[pdf]](https://www.semanticscholar.org/paper/Deep-Learning-Based-Abstractive-Text-Summarization%3A-Suleiman-Awajan/b7da726c244287748575ef404009609afde45bea)

25. **A Survey of Knowledge-Enhanced Text Generation** *Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang* [[pdf]](https://arxiv.org/abs/2010.04389)

26. **From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information** *Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan* `IJCAI20` [[pdf]](https://arxiv.org/abs/2005.04684)

27. **Neural Abstractive Text Summarization with Sequence-to-Sequence Models** *Tian Shi, Yaser Keneshloo, Naren Ramakrishnan, Chandan K. Reddy* [[pdf]](https://arxiv.org/abs/1812.02303)

28. **A Survey on Neural Network-Based Summarization Methods** *Yue Dong* [[pdf]](https://arxiv.org/abs/1804.04589)

29. **Automated text summarisation and evidence-based medicine: A survey of two domains** *Abeed Sarker, Diego Molla, Cecile Paris* [[pdf]](https://arxiv.org/abs/1706.08162)

30. **Automatic Keyword Extraction for Text Summarization: A Survey** *Santosh Kumar Bharti, Korra Sathya Babu* [[pdf]](https://arxiv.org/abs/1704.03242)

31. **Text Summarization Techniques: A Brief Survey** *Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut* [[pdf]](https://arxiv.org/abs/1707.02268)

32. **Recent automatic text summarization techniques: a survey** *Mahak Gambhir, Vishal Gupta* [[pdf]](https://link.springer.com/article/10.1007/s10462-016-9475-9)

## Toolkit

1. **Summary Workbench: Unifying Application and Evaluation of Text Summarization Models** *Shahbaz Syed, Dominik Schwabe, Martin Potthast* `EMNLP 2022 Demo` [[pdf]](https://arxiv.org/abs/2210.09587) [[demo]](https://tldr.demo.webis.de/summarize)   [Abs] This paper presents Summary Workbench, a new tool for developing and evaluating text summarization models. New models and evaluation measures can be easily integrated as Docker-based plugins, allowing to examine the quality of their summaries against any input and to evaluate them using various evaluation measures. Visual analyses combining multiple measures provide insights into the models' strengths and weaknesses. The tool is hosted at \url{this https URL} and also supports local deployment for private resources. 

1. **iFacetSum: Coreference-based Interactive Faceted Summarization for Multi-Document Exploration** *Eran Hirsch, Alon Eirew, Ori Shapira, Avi Caciularu, Arie Cattan, Ori Ernst, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Ido Dagan* `EMNLP 2021` [[pdf]](https://arxiv.org/abs/2109.11621) [[demo]](https://biu-nlp.github.io/iFACETSUM/WebApp/client/)

1. **SummerTime: Text Summarization Toolkit for Non-experts** *Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma, Troy Feng, Yusen Zhang, Tao Yu, Ahmed Hassan Awadallah, Dragomir Radev* `EMNLP 2021 Demo Track` [[pdf]](https://arxiv.org/abs/2108.12738) [[Demo]](https://github.com/Yale-LILY/SummerTime)

1. **Summary Explorer: Visualizing the State of the Art in Text Summarization** *Shahbaz Syed, Tariq Yousef, Khalid Al-Khatib, Stefan Jänicke, Martin Potthast*  [[pdf]](https://arxiv.org/abs/2108.01879) [[web]](https://tldr.webis.de/)

1. **fastnlp/fastSum** [[code]](https://github.com/fastnlp/fastSum)

1. **Graph4NLP** [[code]](https://github.com/graph4ai/graph4nlp) [[summarization]](https://github.com/graph4ai/graph4nlp/tree/master/examples/pytorch/summarization)

1. **CTRLsum: Towards Generic Controllable Text Summarization** [[pdf]](https://arxiv.org/abs/2012.04281) [[code]](https://github.com/hyunwoongko/summarizers) `EMNLP 2022`   [Abs] Current summarization systems yield generic summaries that are disconnected from users’ preferences and expectations. To address this limitation, we present CTRLsum, a generic framework to control generated summaries through a set of keywords. During training keywords are extracted automatically without requiring additional human annotations. At test time CTRLsum features a control function to map control signal to keywords; through engineering the control function, the same trained model is able to be applied to control summaries on various dimensions, while neither affecting the model training process nor the pretrained models. We additionally explore the combination of keywords and text prompts for more control tasks. Experiments demonstrate the effectiveness of CTRLsum on three domains of summarization datasets and five control tasks: (1) entity-centric and (2) length-controllable summarization, (3) contribution summarization on scientific papers, (4) invention purpose summarization on patent filings, and (5) question-guided summarization on news articles. Moreover, when used in a standard, unconstrained summarization setting, CTRLsum is comparable or better than strong pretrained systems. 

1. **OpenNMT-py: Open-Source Neural Machine Translation** [[pdf]](https://www.aclweb.org/anthology/W18-1817.pdf) [[code]](https://github.com/OpenNMT/OpenNMT-py)

2. **Fairseq: Facebook AI Research Sequence-to-Sequence Toolkit written in Python.**  [[code]](https://github.com/pytorch/fairseq)

3. **LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization** *Tian Shi, Ping Wang, Chandan K. Reddy* `NAACL19` [[pdf]](https://www.aclweb.org/anthology/N19-4012/) [[code]](https://github.com/tshi04/LeafNATS)

4. **TransformerSum** [[code]](https://github.com/HHousen/TransformerSum)

## Analysis

![](https://img.shields.io/badge/Analysis-analysis-red) ![](https://img.shields.io/badge/Meta%20Evaluation-evaluation-brightgreen) ![](https://img.shields.io/badge/Bias-bias-orange) ![](https://img.shields.io/badge/Architecture-architecture-blue)

1. **Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation** *Raymond Li, Wen Xiao, Linzi Xing, Lanjun Wang, Gabriel Murray, Giuseppe Carenini* `EMNLP 2022` [[pdf]](https://aclanthology.org/2022.emnlp-main.694/) [[code]](https://github.com/raymondzmc/Attention-Pattern-Exploitation)  [Abs] The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why and how transformers work. In another vein, researchers propose new attention augmentation methods to make transformers more accurate, efficient and interpretable. In this paper, we combine these two lines of research in a human-in-the-loop pipeline to first discover important task-specific attention patterns. Then those patterns are injected, not only to smaller models, but also to the original model. The benefits of our pipeline and discovered patterns are demonstrated in two case studies with extractive summarization and topic segmentation. After discovering interpretable patterns in BERT-based models fine-tuned for the two downstream tasks, experiments indicate that when we inject the patterns into attention heads, the models show considerable improvements in accuracy and efficiency. 

1. **Analyzing Multi-Task Learning for Abstractive Text Summarization** *Frederic Kirstein, Jan Philip Wahle, Terry Ruas, Bela Gipp* `` [[pdf]](https://arxiv.org/abs/2210.14606)  [Abs] Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text summarization. Task families are a form of task grouping during the pre-finetuning stage to learn common skills, such as reading comprehension. To close this gap, we analyze the influence of multi-task learning strategies using task families for the English abstractive text summarization task. We group tasks into one of three strategies, i.e., sequential, simultaneous, and continual multi-task learning, and evaluate trained models through two downstream tasks. We find that certain combinations of task families (e.g., advanced reading comprehension and natural language inference) positively impact downstream performance. Further, we find that choice and combinations of task families influence downstream performance more than the training scheme, supporting the use of task families for abstractive text summarization. 

1. **On Decoding Strategies for Neural Text Generators** *Gian Wiher, Clara Meister, Ryan Cotterell* [[pdf]](https://arxiv.org/abs/2203.15721) 

1. **Training Dynamics for Text Summarization Models** *Tanya Goyal, Jiacheng Xu, Junyi Jessy Li, Greg Durrett* [https://arxiv.org/abs/2110.08370]

1. **Does Summary Evaluation Survive Translation to Other Languages?** *Neslihan Iskender, Oleg Vasilyev, Tim Polzehl, John Bohannon, Sebastian Möller* [[pdf]](https://arxiv.org/abs/2109.08129)

1. **How well do you know your summarization datasets?** *Priyam Tejaswin, Dhruv Naik, Pengfei Liu* `Findings of ACL 2021` [[pdf]](https://arxiv.org/abs/2106.11388) [[code]](https://github.com/priyamtejaswin/howwelldoyouknow)

1. **Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution** *Jiacheng Xu, Greg Durrett* `ACL2021` [[pdf]](https://aclanthology.org/2021.acl-long.539/) [[code]](https://github.com/jiacheng-xu/sum-interpret)

1. **To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text** *Matt Wilber, William Timkey, Marten Van Schijndel* `Findings of ACL 2021` [[pdf]](https://arxiv.org/abs/2106.01581) [[code]](https://github.com/mwilbz/pointer-generator-analysis)

1. **What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization** *Maartje ter Hoeve, Julia Kiseleva, Maarten de Rijke* [[pdf]](https://arxiv.org/abs/2012.07619)

1. **Intrinsic Evaluation of Summarization Datasets** *Rishi Bommasani, Claire Cardie* `EMNLP20` [[pdf]](https://www.aclweb.org/anthology/2020.emnlp-main.649/) ![](https://img.shields.io/badge/-analysis-red)

1. **Metrics also Disagree in the Low Scoring Range: Revisiting Summarization Evaluation Metrics** *Manik Bhandari, Pranav Gour, Atabak Ashfaq, Pengfei Liu* `COLING20 Short` [[pdf]](https://arxiv.org/abs/2011.04096) [[code]](https://github.com/manikbhandari/RevisitSummEvalMetrics) ![](https://img.shields.io/badge/-analysis-red)

1. **At Which Level Should We Extract? An Empirical Analysis on Extractive Document Summarization** *Qingyu Zhou, Furu Wei, Ming Zhou* `COLING20` [[pdf]](https://arxiv.org/abs/2004.02664) ![](https://img.shields.io/badge/-analysis-red)

1. **Corpora Evaluation and System Bias detection in Multi Document Summarization** *Alvin Dey, Tanya Chowdhury, Yash Kumar, Tanmoy Chakraborty* `Findings of EMNLP` [[pdf]](https://www.aclweb.org/anthology/2020.findings-emnlp.254/) ![](https://img.shields.io/badge/-analysis-red)

1. **Understanding the Extent to which Summarization Evaluation Metrics Measure the Information Quality of Summaries** *Daniel Deutsch, Dan Roth* [[pdf]](https://arxiv.org/abs/2010.12495) [[code]](https://github.com/CogComp/content-analysis-experiments)![](https://img.shields.io/badge/-analysis-red)

1. **Understanding Neural Abstractive Summarization Models via Uncertainty** *Jiacheng Xu, Shrey Desai, Greg Durrett* `EMNLP20 Short` [[pdf]](https://arxiv.org/abs/2010.07882) [[code]](https://github.com/jiacheng-xu/text-sum-uncertainty) ![](https://img.shields.io/badge/-analysis-red)

2. **Re-evaluating Evaluation in Text Summarization** *Manik Bhandari, Pranav Gour, Atabak Ashfaq, Pengfei Liu, Graham Neubig* `EMNLP20` [[pdf]](https://arxiv.org/abs/2010.07100) [[code]](https://github.com/neulab/REALSumm) ![](https://img.shields.io/badge/-evaluation-brightgreen)

3. **CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems** *Yiran Chen, Pengfei Liu, Ming Zhong, Zi-Yi Dou, Danqing Wang, Xipeng Qiu, Xuanjing Huang* `EMNLP20` [[pdf]](https://arxiv.org/abs/2010.05139) [[code]](https://github.com/zide05/CDEvalSumm) ![](https://img.shields.io/badge/-evaluation-brightgreen)

4. **What Have We Achieved on Text Summarization?** *Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, Yue Zhang* `EMNLP20` [[pdf]](https://arxiv.org/abs/2010.04529) ![](https://img.shields.io/badge/-analysis-red)

5. **Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization** *Zhengyuan Liu, Ke Shi, Nancy F. Chen* `Findings of EMNLP20` [[pdf]](https://arxiv.org/abs/2004.13983) ![](https://img.shields.io/badge/-bias-orange)

6. **Extractive Summarization as Text Matching** *Ming Zhong, Pengfei Liu, Yiran Chen, Danqing Wang, Xipeng Qiu, Xuanjing Huang* `ACL20` [[pdf]](https://arxiv.org/abs/2004.08795) [[code]](https://github.com/maszhongming/MatchSum) ![](https://img.shields.io/badge/-architecture-blue) ![](https://img.shields.io/badge/-bias-orange)

7. **Neural Text Summarization: A Critical Evaluation** *Wojciech Kryściński, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, Richard Socher* `EMNLP19` [[pdf]](https://www.aclweb.org/anthology/D19-1051/) ![](https://img.shields.io/badge/-analysis-red)

8. **Earlier Isn’t Always Better:Sub-aspect Analysis on Corpus and System Biases in Summarization** *Taehee Jung, Dongyeop Kang, Lucas Mentch, Eduard Hovy* `EMNLP19` [[pdf]](https://arxiv.org/abs/1908.11723) [[code]](https://github.com/dykang/biassum) ![](https://img.shields.io/badge/-bias-orange)

9. **A Closer Look at Data Bias in Neural Extractive Summarization Models** *Ming Zhong, Danqing Wang, Pengfei Liu, Xipeng Qiu, Xuanjing Huang* `EMNLP19 Workshop` [[pdf]](https://arxiv.org/abs/1909.13705) ![](https://img.shields.io/badge/-bias-orange)

10. **Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses** *Matt Grenander, Yue Dong, Jackie Chi Kit Cheung, Annie Louis* `EMNLP19 Short` [[pdf]](https://arxiv.org/abs/1909.04028)  ![](https://img.shields.io/badge/-bias-orange)

11. **Searching for Effective Neural Extractive Summarization: What Works and What's Next** *Ming Zhong, Pengfei Liu, Danqing Wang, Xipeng Qiu, Xuanjing Huang* `ACL19` [[pdf]](https://arxiv.org/abs/1907.03491) [[code]](https://github.com/maszhongming/Effective_Extractive_Summarization) ![](https://img.shields.io/badge/-architecture-blue)

12. **Content Selection in Deep Learning Models of Summarization** *Chris Kedzie, Kathleen McKeown, Hal Daumé III* `EMNLP18` [[pdf]](https://www.aclweb.org/anthology/D18-1208/) [[code]](https://github.com/kedz/nnsum/tree/emnlp18-release) ![](https://img.shields.io/badge/-architecture-blue)

## Thesis

1. **Principled Approaches to Automatic Text Summarization** *Maxime Peyrard* [[pdf]](https://tuprints.ulb.tu-darmstadt.de/9012/)

2.  **Neural Text Summarization and Generation** *Piji Li* [[pdf]](http://lipiji.com/docs/thesis.pdf)

## Theory

1. **Bayesian Active Summarization** *Alexios Gidiotis, Grigorios Tsoumakas* [[pdf]](https://arxiv.org/abs/2110.04480)

1. **RefSum: Refactoring Neural Summarization** *Yixin Liu, Zi-Yi Dou, Pengfei Liu* `NAACL21` [[pdf]](https://arxiv.org/abs/2104.07210) [[code]](https://github.com/yixinL7/Refactoring-Summarization)

1. **Principled Approaches to Automatic Text Summarization** *Maxime Peyrard* [[pdf]](https://tuprints.ulb.tu-darmstadt.de/9012/) ![](https://img.shields.io/badge/-thesis-red)

1. **KLearn: Background Knowledge Inference from Summarization Data** *Maxime Peyrard, Robert West* `Findings of EMNLP20` [[pdf]](https://arxiv.org/abs/2010.06213) [[code]](https://github.com/epfl-dlab/KLearn)

2. **A Simple Theoretical Model of Importance for Summarization** *Maxime Peyrard* `ACL19` [[pdf]](https://www.aclweb.org/anthology/P19-1101/)

3. **BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle** *Peter West, Ari Holtzman, Jan Buys, Yejin Choi* `EMNLP19` [[pdf]](https://arxiv.org/abs/1909.07405) [[code]](https://github.com/peterwestuw/BottleSum)

## Dataset

|ID|Name|Description|Paper|Conference|

|:---:|:---:|:---:|:---:|:---:|

| 1 | [CNN-DailyMail](https://github.com/harvardnlp/sent-summary) | News | [Abstractive Text Summarization using Sequence\-to\-sequence RNNs and Beyond ](https://www.aclweb.org/anthology/K16-1028/)|SIGNLL16|

| 2 | [New York Times](https://catalog.ldc.upenn.edu/LDC2008T19)| News | [The New York Times Annotated Corpus](https://catalog.ldc.upenn.edu/LDC2008T19) ||

| 3 | [DUC](https://duc.nist.gov/data.html)| News | [The Effects Of Human Variation In DUC Summarization Evaluation](https://www.aclweb.org/anthology/W04-1003/) ||

| 4 | [Gigaword](https://github.com/harvardnlp/sent-summary) | News | [A Neural Attention Model For Abstractive Sentence Summarization](https://arxiv.org/abs/1509.00685) |EMNLP15|

| 5 | [Newsroom](http://lil.nlp.cornell.edu/newsroom/) | News | [Newsroom: A Dataset of 1\.3 Million Summaries with Diverse Extractive Strategies](https://www.aclweb.org/anthology/N18-1065)|NAACL18|

| 6 | [Xsum](https://github.com/EdinburghNLP/XSum) | News | [Don’t Give Me the Details, Just the Summary\! Topic\-Aware Convolutional Neural Networks for Extreme Summarization](https://www.aclweb.org/anthology/D18-1206/)|EMNLP18|

| 7 | [Multi-News](https://github.com/Alex-Fabbri/Multi-News)| Multi-document News | [Multi\-News: a Large\-Scale Multi\-Document Summarization Dataset and Abstractive Hierarchical Model](https://arxiv.org/abs/1906.01749)|ACL19|

| 8 | [SAMSum](https://arxiv.org/abs/1911.12237)| Multi-party conversation | [SAMSum Corpus: A Human\-annotated Dialogue Dataset for Abstractive Summarization](https://arxiv.org/abs/1911.12237)|EMNLP19|

| 9 | [AMI](http://groups.inf.ed.ac.uk/ami/download/) | Meeting | [The AMI Meeting Corpus: A pre\-announcement\. ](http://groups.inf.ed.ac.uk/ami/download/)||

| 10 | [ICSI](http://groups.inf.ed.ac.uk/ami/icsi/download/)| Meeting | [The ICSI Meeting Corpus](http://groups.inf.ed.ac.uk/ami/icsi/) ||

| 11 | [MSMO](http://www.nlpr.ia.ac.cn/cip/jjzhang.htm)| Multi-modal | [MSMO: Multimodal Summarization with Multimodal Output](https://www.aclweb.org/anthology/D18-1448/) |EMNLP18|

| 12 | [How2](https://github.com/srvk/how2-dataset) | Multi-modal | [How2: A Large\-scale Dataset for Multimodal Language Understanding](https://arxiv.org/abs/1811.00347)| NIPS18|

| 13 | [ScisummNet](https://cs.stanford.edu/~myasu/projects/scisumm_net/) | Scientific paper | [ScisummNet: A Large Annotated Corpus and Content\-Impact Models for Scientific Paper Summarization with Citation Networks](https://arxiv.org/abs/1909.01716) |AAAI19|

| 14 | [PubMed, ArXiv](https://github.com/armancohan/long-summarization)| Scientific paper | [A Discourse\-Aware Attention Model for Abstractive Summarization of Long Documents](https://arxiv.org/abs/1804.05685)| NAACL18 |

| 15 | [TALKSUMM](https://github.com/levguy/talksumm) | Scientific paper | [TALKSUMM: A Dataset and Scalable Annotation Method for Scientiﬁc Paper Summarization Based on Conference Talks](https://www.aclweb.org/anthology/P19-1204/) | ACL19 |

| 16 | [BillSum](https://github.com/FiscalNote/BillSum) | Legal | [BillSum: A Corpus for Automatic Summarization of US Legislation](https://www.aclweb.org/anthology/D19-5406/) |EMNLP19|

| 17 | [LCSTS](http://icrc.hitsz.edu.cn/Article/show/139.html)![](https://img.shields.io/badge/-Chinese-orange)| Chinese Weibo| [LCSTS: A Large Scale Chinese Short Text Summarization Dataset ](https://www.aclweb.org/anthology/D15-1229/)|EMNLP15|

| 18 | [WikiHow](https://github.com/mahnazkoupaee/WikiHow-Dataset)| Online Knowledge Base | [WikiHow: A Large Scale Text Summarization Dataset](https://arxiv.org/abs/1810.09305) ||

| 19 | [Concept-map-based MDS Corpus](https://github.com/UKPLab/emnlp2017-cmapsum-corpus/)| Educational Multi-document| [Bringing Structure into Summaries : Crowdsourcing a Benchmark Corpus of Concept Maps](https://www.aclweb.org/anthology/D17-1320/)|EMNLP17|

| 20 | [WikiSum](https://github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikisum) | Wikipedia Multi-document | [Generating Wikipedia By Summarizing Long Sequence](https://arxiv.org/abs/1801.10198) |ICLR18|

| 21 | [GameWikiSum](https://github.com/Diego999/GameWikiSum) | Game Multi-document | [GameWikiSum : a Novel Large Multi\-Document Summarization Dataset](https://arxiv.org/abs/2002.06851) |LREC20|

| 22 | [En2Zh CLS, Zh2En CLS](http://www.nlpr.ia.ac.cn/cip/dataset.htm)![](https://img.shields.io/badge/-Chinese-orange)| Cross-Lingual | [NCLS: Neural Cross\-Lingual Summarization](https://arxiv.org/abs/1909.00156) |EMNLP19|

| 23 | [Timeline Summarization Dataset](https://github.com/yingtaomj/Learning-towards-Abstractive-Timeline-Summarization)| Baidu timeline| [Learning towards Abstractive Timeline Summarization ](https://www.ijcai.org/Proceedings/2019/686)|IJCAI19|

| 24 | [Reddit TIFU](https://github.com/ctr4si/MMN) | online discussion | [Abstractive Summarization of Reddit Posts with Multi\-level Memory Networks](https://arxiv.org/abs/1811.00783)| NAACL19 |

| 25 | [TripAtt](https://github.com/Junjieli0704/ASN) | Review | [Attribute\-aware Sequence Network for Review Summarization](https://www.aclweb.org/anthology/D19-1297/)|EMNLP19|

| 26 | [Reader Comments Summarization Corpus](https://drive.google.com/file/d/1_YH5cBtvNnUNJjGj7kiTMjuHydBqWYQT/view?usp=drive_open) | Comments-based Weibo | [Abstractive Text Summarization by Incorporating Reader Comments ](https://arxiv.org/abs/1812.05407)|AAAI19|

| 27 | [BIGPATENT](https://evasharma.github.io/bigpatent/) | Patent| [BIGPATENT: A Large\-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)|ACL19|

| 28 | [Curation Corpus](https://github.com/CurationCorp/curation-corpus) | News | [Curation Corpus for Abstractive Text Summarisation](https://github.com/CurationCorp/curation-corpus) ||

| 29 | [MATINF](https://github.com/WHUIR/MATINF)![](https://img.shields.io/badge/-Chinese-orange)|Multi-task|[MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization](https://arxiv.org/abs/2004.12302)|ACL20|

| 30 | [MLSUM](https://github.com/recitalAI/MLSUM) |Multi-Lingual Summarization Dataset|[MLSUM: The Multilingual Summarization Corpus](https://arxiv.org/abs/2004.14900)|EMNLP20|

| 31 | Dialogue(Debate)|Argumentative Dialogue Summary Corpus |[Using Summarization to Discover Argument Facets in Online Idealogical Dialog](https://www.aclweb.org/anthology/N15-1046/)|NAACL15|

|32|[WCEP](https://github.com/complementizer/wcep-mds-dataset)|News Multi-document|[A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal](https://arxiv.org/abs/2005.10070)|ACL20 Short|

|33|[ArgKP](https://www.research.ibm.com/haifa/dept/vst/debating_data.shtml)|Argument-to-key Point Mapping|[From Arguments to Key Points: Towards Automatic Argument Summarization](https://arxiv.org/abs/2005.01619)|ACL20|

|34|[CRD3](https://github.com/RevanthRameshkumar/CRD3)|Dialogue|[Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset](https://www.aclweb.org/anthology/2020.acl-main.459/)|2020|

|35|[Gazeta](https://github.com/IlyaGusev/gazeta)|Russian news|[Dataset for Automatic Summarization of Russian News](https://arxiv.org/abs/2006.11063)||

|36|[MIND](https://msnews.github.io/)|English news recommendation, Summarization, Classification, Entity|[MIND: A Large-scale Dataset for News Recommendation](https://www.aclweb.org/anthology/2020.acl-main.331/)|ACL20|

|37|[public_meetings](https://github.com/pltrdy/autoalign)|french meeting(test set)|[Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation](https://www.aclweb.org/anthology/2020.lrec-1.829)|LREC|

|38|Enron|Email|[Building a Dataset for Summarization and Keyword Extraction from Emails](https://www.aclweb.org/anthology/L14-1028/)|2014|

|39|Columbia|Email|[Summarizing Email Threads]([https://www.aclweb.org/anthology/N04-4027.pdf](https://dl.acm.org/doi/10.5555/1613984.1614011))|2004|

|40|[BC3](https://www.cs.ubc.ca/cs-research/lci/research-groups/natural-language-processing/bc3.html)|Email|[A publicly available annotated corpus for supervised email summarization](https://www.ufv.ca/media/assets/computer-information-systems/gabriel-murray/publications/aaai08.pdf)||

|41|[WikiLingua](https://github.com/esdurmus/Wikilingua)![](https://img.shields.io/badge/-Chinese-orange)|Cross-Lingual|[WikiLingua- A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://arxiv.org/abs/2010.03093)|Findings of EMNLP20|

|42|[LcsPIRT](http://eie.usts.edu.cn/prj/NLPoSUST/LcsPIRT.htm)![](https://img.shields.io/badge/-Chinese-orange)|Chinese Dialogue|[Global Encoding for Long Chinese Text Summarization](https://dl.acm.org/doi/10.1145/3407911)|TALLIP|

|43|[CLTS](https://github.com/lxj5957/CLTS-Dataset)，[CLTS-plus](https://github.com/lxj5957/CLTS-plus-Dataset)![](https://img.shields.io/badge/-Chinese-orange)|Chinese News|[CLTS: A New Chinese Long Text Summarization Dataset](https://link.springer.com/chapter/10.1007/978-3-030-60450-9_42) [CLTS+: A New Chinese Long Text Summarization Dataset with Abstractive Summaries](https://arxiv.org/abs/2206.04253)|NLPCC20|

|44|[VMSMO](https://github.com/yingtaomj/VMSMO)|Multi-modal|[VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles](https://arxiv.org/abs/2010.05406)|EMNLP20 |

|45|[Multi-XScience](https://github.com/yaolu/Multi-XScience)|Multi-document|[Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientiﬁc Articles](https://arxiv.org/abs/2010.14235)|EMNLP20 short|

|46|[SCITLDR](https://github.com/allenai/scitldr)|Scientific Document|[TLDR: Extreme Summarization of Scientific Documents](https://arxiv.org/abs/2004.15011)|Findings of EMNLP20|

|47|[scisumm-corpus](https://github.com/WING-NUS/scisumm-corpus)|Scientific Document|||

|48|[QBSUM](https://www.dropbox.com/sh/t2cp7ml1kb8ako0/AADmS2RMfJvLbukyQbb08CGGa?dl=0)![](https://img.shields.io/badge/-Chinese-orange)|Query-Based Chinese|[QBSUM: a Large-Scale Query-Based Document Summarization Dataset from Real-world Applications](https://arxiv.org/abs/2010.14108)|Computer Speech & Language|

|49|[qMDS](https://github.com/google-research-datasets/aquamuse)|Query-Based Multi-Document|[AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization](https://arxiv.org/abs/2010.12694)||

|50|[Liputan6](https://github.com/fajri91/sum_liputan6)|Indonesian|[Liputan6: A Large-scale Indonesian Dataset for Text Summarization](https://arxiv.org/pdf/2011.00679.pdf)|AACL20|

|51|[SportsSum](https://github.com/ej0cl6/SportsSum)![](https://img.shields.io/badge/-Chinese-orange)|Sports Game|[Generating Sports News from Live Commentary: A Chinese Dataset for Sports Game Summarization](https://khhuang.me/docs/aacl2020sportssum.pdf)|AACL20|

|52|[WikiAsp](https://github.com/neulab/wikiasp)|Aspect-based|[WikiAsp: A Dataset for Multi-domain Aspect-based Summarization](https://arxiv.org/abs/2011.07832)|Transaction of the ACL|

|53|[DebateSum](https://github.com/Hellisotherpeople/DebateSum)![](https://img.shields.io/badge/-Query%20Focused-purple)|argument|[DebateSum:A large-scale argument mining and summarization dataset](https://arxiv.org/abs/2011.07251)|ARGMIN 2020|

|54|[Open4Business](https://github.com/amanpreet692/Open4Business)|Business|[Open4Business (O4B): An Open Access Dataset for Summarizing Business Documents](https://arxiv.org/abs/2011.07636)|Workshop on Dataset Curation and Security-NeurIPS 2020|

|55|[OrangeSum](https://github.com/moussaKam/OrangeSum)|French|[BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321)||

|56|[Medical Conversation](https://github.com/cuhksz-nlp/HET-MC)![](https://img.shields.io/badge/-Chinese-orange)|medical conversation|[Summarizing Medical Conversations via Identifying Important Utterances](https://www.aclweb.org/anthology/2020.coling-main.63/)|COLING20|

|57|[SumTitles](https://github.com/huawei-noah/sumtitles)|movie dialogue|[SumTitles: a Summarization Dataset with Low Extractiveness](https://www.aclweb.org/anthology/2020.coling-main.503/)|COLING20|

|58|[BANS](https://www.kaggle.com/datasets/prithwirajsust/bengali-news-summarization-dataset)|bengali news|[Bengali Abstractive News Summarization (BANS): A Neural Attention Approach]()|TCCE-2020|

|59|[e-commerce](https://github.com/ypnlp/coling)![](https://img.shields.io/badge/-Chinese-orange)|E-commerce|[On the Faithfulness for E-commerce Product Summarization](https://www.aclweb.org/anthology/2020.coling-main.502/)|COLING20|

|60|[TWEETSUM]()|Twitter|[TWEETSUM: Event-oriented Social Summarization Dataset](https://www.aclweb.org/anthology/2020.coling-main.504/)|COLING20|

|61|[SPACE](https://github.com/stangelid/qt)|Opinion|[Extractive Opinion Summarization in Quantized Transformer Spaces](https://arxiv.org/abs/2012.04443)|TACL|

|62|[pn-summary](https://github.com/hooshvare/pn-summary)|Persian|[Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization](https://arxiv.org/abs/2012.11204)|csicc2021|

|63|[E-commerce1](https://github.com/RowitZou/topic-dialog-summ)*desensitized*|Dialogue|[Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling](https://arxiv.org/abs/2012.07311)|AAAI21|

|64|[E-commerce2](https://github.com/RowitZou/RankAE)*desensitized*|Dialogue|[Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders](https://arxiv.org/abs/2012.07300)|AAAI21|

|65|[BengaliSummarization](https://github.com/tafseer-nayeem/BengaliSummarization)|Bengali|[Unsupervised Abstractive Summarization of Bengali Text Documents](https://arxiv.org/abs/2102.04490)|EACL21|

|66|[MediaSum](https://github.com/zcgzcgzcg1/MediaSum)|Dialogue|[MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://arxiv.org/abs/2103.06410)|NAACL21|

|67|[Healthline and BreastCancer](https://github.com/darsh10/Nutribullets)|multi-document|[Nutri-bullets: Summarizing Health Studies by Composing Segments](https://arxiv.org/abs/2103.11921)|AAAI21|

|68|[GOVREPORT](https://gov-report-data.github.io/)|Long Government reports|[Efficient Attentions for Long Document Summarization](https://arxiv.org/abs/2104.02112)|NAACL21|

|69|[SSN](https://github.com/ChenxinAn-fdu/CGSum)|Scientific Paper|[Enhancing Scientific Papers Summarization with Citation Graph](https://arxiv.org/abs/2104.03057)|AAAI21|

|70|[MTSamples](https://github.com/babylonhealth/medical-note-summarisation)|Medical|[Towards objectively evaluating the quality of generated medical summaries](https://arxiv.org/abs/2104.04412)||

|71|[QMSum](https://github.com/Yale-LILY/QMSum)|Meeting, Query|[QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://arxiv.org/abs/2104.05938)|NAACL21|

|72|[MS2](https://github.com/allenai/ms2)|Medical, Multi-Document|[MS2: Multi-Document Summarization of Medical Studies](https://arxiv.org/abs/2104.06486)||

|73|[SummScreen](https://github.com/mingdachen/SummScreen)|Television Series|[SummScreen: A Dataset for Abstractive Screenplay Summarization](https://aclanthology.org/2022.acl-long.589/)|ACL 2022|

|74|[SciDuet](https://github.com/IBM/document2slides)|Scientific Papers and Slides|[D2S: Document-to-Slide Generation Via Query-Based Text Summarization](https://github.com/IBM/document2slides)|NAACL21|

|75|[MultiHumES](https://deephelp.zendesk.com/hc/en-us/sections/360011925552-MultiHumES)|Multilingual|[MultiHumES: Multilingual Humanitarian Dataset for Extractive Summarization](https://www.aclweb.org/anthology/2021.eacl-main.146/)|EACL21|

|76|[DialSumm](https://github.com/cylnlp/DialSumm)|Dialogue|[DialSumm: A Real-Life Scenario Dialogue Summarization Dataset](https://arxiv.org/abs/2105.06762)|Findings of ACL21|

|77|[BookSum](https://github.com/salesforce/booksum)|Book, Long-form|[BookSum: A Collection of Datasets for Long-form Narrative Summarization](https://arxiv.org/abs/2105.08209)||

|78|[CLES](http://icrc.hitsz.edu.cn/xszy/yjzy.htm)![](https://img.shields.io/badge/-Chinese-orange)|Chinese Weibo |[A Large-Scale Chinese Long-Text Extractive Summarization Corpus](https://ieeexplore.ieee.org/abstract/document/9414946)|ICASSP|

|79|[FacetSum](https://github.com/hfthair/emerald_crawler)|Scientific Paper|[Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents](https://aclanthology.org/2021.acl-short.137/)|ACL2021 short|

|80|[ConvoSumm](https://github.com/Yale-LILY/ConvoSumm)|Dialogue|[ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining](https://aclanthology.org/2021.acl-long.535/)|ACL2021|

|81|[AgreeSum](https://github.com/google-research-datasets/AgreeSum)|Multi-document with entailment annotations|[AgreeSum: Agreement-Oriented Multi-Document Summarization](https://arxiv.org/abs/2106.02278)|Findings of ACL2021|

|82|[En2De](https://github.com/ybai-nlp/MCLAS)|Cross-Lingual En2De|[Cross-Lingual Abstractive Summarization with Limited Parallel Resources](https://arxiv.org/abs/2105.13648)|ACL 2021|

|83|[VT-SSum]()|Spoken|[VT-SSum: A Benchmark Dataset for Video Transcript Segmentation and Summarization](https://arxiv.org/abs/2106.05606)||

|84|[AESLC](https://github.com/ryanzhumich/AESLC)|Email|[This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://www.aclweb.org/anthology/P19-1043/)|ACL 2019|

|85|[XL-Sum](https://github.com/csebuetnlp/xl-sum)|Cross-lingual|[XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](http://rifatshahriyar.github.io/files/XL-Sum.pdf)|Findings of ACL2021|

|86|[TES 2012-2016](https://github.com/JoeBloggsIR/TSSuBERT)|Tweet|[TSSuBERT: Tweet Stream Summarization Using BERT](https://arxiv.org/abs/2106.08770)||

|87|[PENS](https://msnews.github.io/pens.html)|Personalized Headline|[PENS: A Dataset and Generic Framework for Personalized News Headline Generation](https://www.microsoft.com/en-us/research/uploads/prod/2021/06/ACL2021_PENS_Camera_Ready_1862_Paper.pdf)|ACL 2021|

|88|[XSum Hallucination Annotations](https://github.com/google-research-datasets/xsum_hallucination_annotations)|Factuality|[On Faithfulness and Factuality in Abstractive Summarization](https://arxiv.org/abs/2005.00661)|ACL 2020|

|89|[factuality-datasets](https://github.com/tagoyal/factuality-datasets#factuality-datasets)|Factuality|[Annotating and Modeling Fine-grained Factuality in Summarization](https://arxiv.org/abs/2104.04302)|NAACL 2021|

|90|[frank](https://github.com/artidoro/frank)|Factuality|[Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://arxiv.org/abs/2104.13346)|NAACL 2021|

|91|[TRIPOD](https://github.com/ppapalampidi/GraphTP)|Movie|[Movie Summarization via Sparse Graph Construction](https://arxiv.org/abs/2012.07536)|AAAI 2021|

|92|[AdaptSum](https://github.com/TysonYu/AdaptSum)|Low-Resource|[AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization](https://arxiv.org/abs/2103.11332)|NAACL 2021|

|93|[PTS](https://github.com/FeiSun/ProductTitleSummarizationCorpus)|Product|[Multi-Source Pointer Network for Product Title Summarization](https://arxiv.org/abs/1808.06885)|CIKM 2018|

|94|[RAMDS](https://github.com/lipiji/vae-salience-ramds)|Reader-Aware|[Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset](https://arxiv.org/abs/1708.01065)|EMNLP 2017 Workshop|

|95|[court judgment](https://github.com/gsh199449/proto-summ)|court judgment|[How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing](https://arxiv.org/abs/1909.08837)|EMNLP 2019|

|96|[ADEGBTS](https://github.com/MMLabTHUSZ/ADEGBTS)|gaze behaviors|[A Dataset for Exploring Gaze Behaviors in Text Summarization](https://dl.acm.org/doi/abs/10.1145/3339825.3394928)|ACM MMSys'20|

|97|[MeQSum](https://github.com/abachaa/MeQSum)|Medical|[On the Summarization of Consumer Health Questions](https://www.aclweb.org/anthology/P19-1215/)|ACL 2019|

|98|[OpoSum](https://github.com/stangelid/oposum)|Opinion|[Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://www.aclweb.org/anthology/D18-1403/)|EMNLP 2018|

|99|[MM-AVS](https://github.com/xiyan524/MM-AVS)|Multi-modal|[Multi-modal Summarization for Video-containing Documents](https://arxiv.org/abs/2009.08018)|NAACL 2021|

|100|[WikiCatSum](https://github.com/lauhaide/WikiCatSum)|multi-doc|[Generating Summaries with Topic Templates and Structured Convolutional Decoders](https://arxiv.org/abs/1906.04687)|ACL 2019|

|101|[SDF-TLS](https://github.com/MorenoLaQuatra/SDF-TLS)|Timeline|[Summarize Dates First: A Paradigm Shift in Timeline Summarization](https://dl.acm.org/doi/10.1145/3404835.3462954)|SIGIR 2021|

|102|[RWS-Cit](https://github.com/jingqiangchen/RWS-Cit)||[*Automatic generation of related work through summarizing citations](https://onlinelibrary.wiley.com/doi/epdf/10.1002/cpe.4261)|2017|

|103|[MTLS](https://yiyualt.github.io/mtlsdata/)|Timeline|[Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries](https://aclanthology.org/2021.acl-long.32/)|ACL 2021|

|104|[EMAILSUM](https://github.com/ZhangShiyue/EmailSum)|Email|[EmailSum: Abstractive Email Thread Summarization](https://aclanthology.org/2021.acl-long.537/)|ACL 2021|

|105|[WikiSum](https://registry.opendata.aws/wikisum/)|WikiHow|[WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation](https://aclanthology.org/2021.acl-short.28/)|ACL 2021 Short|

|106|[SumPubMed](https://github.com/vgupta123/sumpubmed)|PubMed Scientific Article|[SumPubMed: Summarization Dataset of PubMed Scientific Articles](https://aclanthology.org/2021.acl-srw.30/)|ACL 2021 Student Research Workshop|

|107|[MLGSum](https://github.com/brxx122/CALMS)|Multi-lingual|[Contrastive Aligned Joint Learning for Multilingual Summarization](https://aclanthology.org/2021.findings-acl.242/)|ACL 2021 Findings|

|108|[SMARTPHONE,COMPUTER](https://github.com/JD-AI-Research-NLP/CUSTOM)|Product|[CUSTOM: Aspect-Oriented Product Summarization for E-Commerce](https://arxiv.org/abs/2108.08010)||

|109|[CSDS](https://github.com/xiaolinAndy/CSDS)|Customer Service Dialogue|[CSDS: A Fine-grained Chinese Dataset for Customer Service Dialogue Summarization](https://arxiv.org/abs/2108.13139)|EMNLP 2021|

|110|[persian-dataset](https://github.com/mohammadiahmad/persian-dataset)|persian|[ARMAN: Pre-training with Semantically Selecting and Reordering of Sentences for Persian Abstractive Summarization](https://arxiv.org/abs/2109.04098)||

|111|[StreamHover](https://github.com/ucfnlp/streamhover)|spoken livestream|[StreamHover: Livestream Transcript Summarization and Annotation](https://arxiv.org/abs/2109.05160)|EMNLP 2021|

|112|[CNewSum](https://dqwang122.github.io/projects/CNewSum/)![](https://img.shields.io/badge/-Chinese-orange)|News|[CNewSum: A Large-scale Chinese News Summarization Dataset with Human-annotated Adequacy and Deducibility Level](https://lileicc.github.io/pubs/wang2021cnewsum.pdf)|NLPCC 2021|

|113|[MiRANews](https://github.com/XinnuoXu/MiRANews)|news, factual|[MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization](https://arxiv.org/abs/2109.10650)|EMNLP 2021 Findings|

|114|[HowSumm](https://github.com/odelliab/HowSumm)|query multi-doc|[HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles](https://arxiv.org/abs/2110.03179)||

|115|[SportsSum2.0](https://github.com/krystalan/SportsSum2.0)|Sports|[SportsSum2.0: Generating High-Quality Sports News from Live Text Commentary](https://arxiv.org/abs/2110.05750)||

|116|[CoCoSum](https://github.com/megagonlabs/cocosum)|opinion multi-ref|[Comparative Opinion Summarization via Collaborative Decoding](https://arxiv.org/abs/2110.07520)||

|117|[MReD](https://github.com/Shen-Chenhui/MReD/)|Controllable|[MReD: A Meta-Review Dataset for Controllable Text Generation](https://arxiv.org/abs/2110.07474)||

|118|[MSˆ2](https://github.com/allenai/ms2)|Multi-Document, Medical|[MSˆ2: Multi-Document Summarization of Medical Studies](https://aclanthology.org/2021.emnlp-main.594/)|EMNLP 2021|

|119|[MassiveSumm](https://github.com/danielvarab/massive-summ)||[MassiveSumm: a very large-scale, very multilingual, news summarisation dataset](https://aclanthology.org/2021.emnlp-main.797/)|EMNLP 2021|

|120|[XWikis](https://github.com/lauhaide/clads)|multilingual|[Models and Datasets for Cross-Lingual Summarisation](https://aclanthology.org/2021.emnlp-main.742/)|EMNLP 2021|

|121|[SUBSUME](https://github.com/afariha/SubSumE)|Intent, subjective|[SUBSUME: A Dataset for Subjective Summary Extraction from Wikipedia Documents](https://aclanthology.org/2021.newsum-1.14/)|EMNLP 2021 newsum|

|122|[TLDR9+](https://github.com/sajastu/reddit_collector)||[TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts](https://aclanthology.org/2021.newsum-1.15/)|EMNLP 2021 newsum|

|123|[20 Minuten](https://github.com/ZurichNLP/20Minuten)|German|[A New Dataset and Efficient Baselines for Document-level Text Simplification in German](https://aclanthology.org/2021.newsum-1.16/)|EMNLP 2021 newsum|

|124|[WSD](https://github.com/MehwishFatimah/wsd)|multi-lingual|[A Novel Wikipedia based Dataset for Monolingual and Cross-Lingual Summarization](https://aclanthology.org/2021.newsum-1.5/)|EMNLP 2021 newsum|

|125|[TEDSummary](https://github.com/nttcslab-sp-admin/TEDSummary)|Speech|[Attention-based Multi-hypothesis Fusion for Speech Summarization](https://arxiv.org/abs/2111.08201)||

|126|[SummaC Benchmark](https://github.com/tingofurro/summac/)|Factual, NLI|[SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization](https://arxiv.org/abs/2111.09525)||

|127|[ForumSum](https://huggingface.co/datasets/forumsum)|Conversation|[ForumSum: A Multi-Speaker Conversation Summarization Dataset](ttps://aclanthology.org/2021.findings-emnlp.391/)|EMNLP 2021 Findings|

|128|[K-SportsSum](https://github.com/krystalan/K-SportsSum)|Sports|[Knowledge Enhanced Sports Game Summarization](https://arxiv.org/abs/2111.12535)|WSDM 2022|

|129|[Test-Amazon](https://github.com/abrazinskas/Copycat-abstractive-opinion-summarizer)|Opinion, New test for Amazon reviews|[Unsupervised Opinion Summarization as Copycat-Review Generation](https://aclanthology.org/2020.acl-main.461/)|ACL 2020|

|130|[Test-Amazon-Yelp](https://github.com/abrazinskas/FewSum)|Opinion, New test for  Amazon(180) and Yelp(300)|[Few-Shot Learning for Opinion Summarization](https://aclanthology.org/2020.emnlp-main.337/)|EMNLP 2020|

|131|[AmaSum](https://github.com/abrazinskas/SelSum)|Opinion|[Learning Opinion Summarizers by Selecting Informative Reviews](https://aclanthology.org/2021.emnlp-main.743/)|EMNLP 2021|

|132|[CrossSum](https://github.com/csebuetnlp/CrossSum)|Cross lingual|[CrossSum: Beyond English-Centric Cross-Lingual Abstractive Text Summarization for 1500+ Language Pairs](https://arxiv.org/abs/2112.08804)||

|133|[HCSCL-MSDataset](https://github.com/LitianD/HCSCL-MSDataset)|Multi-modal|[Hierarchical Cross-Modality Semantic Correlation Learning Model for Multimodal Summarization](https://arxiv.org/abs/2112.12072)|AAAI 2022|

|134|[Klexikon](https://github.com/dennlinger/klexikon)|German|[Klexikon: A German Dataset for Joint Summarization and Simplification](https://arxiv.org/abs/2201.07198)||

|135|[TODSum]()|Customer Service|[TODSum: Task-Oriented Dialogue Summarization with State Tracking](https://arxiv.org/abs/2110.12680)||

|136|[TWEETSUMM](https://aclanthology.org/2021.findings-emnlp.24/)|Customer Service|[TWEETSUMM - A Dialog Summarization Dataset for Customer Service](https://aclanthology.org/2021.findings-emnlp.24/)|Findings of EMNLP 2021|

|137|[PeerSum](https://github.com/oaimli/PeerSum)|Multi-document, Scientific|[PeerSum: A Peer Review Dataset for Abstractive Multi-document Summarization](https://arxiv.org/abs/2203.01769)||

|138|[Celebrity TS, Event TS, Wiki TS](https://github.com/iriscxy/Unified-Timeline-Summarizer)|Timeline, person, event|[Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://dl.acm.org/doi/abs/10.1145/3517221)|TOSI 2022|

|139|[Chart-to-Text](https://github.com/vis-nlp/Chart-to-text)|chart|[Chart-to-Text: A Large-Scale Benchmark for Chart Summarization](https://arxiv.org/abs/2203.06486)||

|140|[GovReport-QS](https://gov-report-data.github.io/)|Long Document|[HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization](https://arxiv.org/abs/2203.10741)|ACL 2022|

|141|[EntSUM](https://zenodo.org/record/6359875)|Entity|[EntSUM: A Data Set for Entity-Centric Summarization](https://github.com/bloomberg/entsum)|ACL 2022|

|142|[ALLSIDES](https://github.com/HLTCHKUST/framing-bias-metric)|Framing Bias|[NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias](https://arxiv.org/abs/2204.04902)|ACL 2022|

|143|[GRAPHELSUMS](https://github.com/maartjeth/summarization_with_graphical_elements)|graph|[Summarization with Graphical Elements](https://arxiv.org/abs/2204.07551)||

|144|[Annotated-Wikilarge-Newsela](https://github.com/AshOlogn/Evaluating-Factuality-in-Text-Simplification)|Factuality|[Evaluating Factuality in Text Simplification](https://arxiv.org/abs/2204.07562)|ACL 2022|

|145|[WikiMulti](https://github.com/tikhonovpavel/wikimulti)|Cross-lingual|[WikiMulti: a Corpus for Cross-Lingual Summarization](https://arxiv.org/abs/2204.11104)||

|146|[Welsh](https://github.com/UCREL/welsh-summarization-dataset)||[Introducing the Welsh Text Summarisation Dataset and Baseline Systems](https://arxiv.org/abs/2205.02545)||

|147|[SuMe](https://stonybrooknlp.github.io/SuMe/)|Biomedical|[SuMe: A Dataset Towards Summarizing Biomedical Mechanisms](https://arxiv.org/abs/2205.04652)|LREC 2022|

|148|[CiteSum](https://github.com/morningmoni/CiteSum)||[CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation](https://arxiv.org/abs/2205.06207)||

|148|[MSAMSum](https://github.com/xcfcode/MSAMSum)|Dialogue|[MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization](https://aclanthology.org/2022.dialdoc-1.1/)|ACL 2022 DialDoc|

|149|[SQuALITY](https://github.com/nyu-mll/SQuALITY)| Long-Document|[SQuALITY: Building a Long-Document Summarization Dataset the Hard Way](https://aclanthology.org/2022.emnlp-main.75/)|EMNLP 2022|

|150|[X-SCITLDR](https://github.com/sobamchan/xscitldr)||[X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documents](https://arxiv.org/abs/2205.15051)|JCDL 2022|

|151|[NEWTS](https://github.com/ali-bahrainian/NEWTS)|News|[NEWTS: A Corpus for News Topic-Focused Summarization](https://arxiv.org/abs/2205.15661)||

|152|[EntSUM](https://github.com/bloomberg/entsum)|Entity|[EntSUM: A Data Set for Entity-Centric Extractive Summarization](https://aclanthology.org/2022.acl-long.237/)|ACL 2022|

|153|[ASPECTNEWS](https://github.com/oja/aosumm)||[ASPECTNEWS: Aspect-Oriented Summarization of News Documents](https://aclanthology.org/2022.acl-long.449/)|ACL 2022|

|154|[RNSum]()|Commit Logs|[RNSum: A Large-Scale Dataset for Automatic Release Note Generation via Commit Logs Summarization](https://aclanthology.org/2022.acl-long.597/)|ACL 2022|

|155|[AnswerSumm](https://github.com/Alex-Fabbri/AnswerSumm)|query multi-doc|[AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization](https://arxiv.org/abs/2111.06474)|NAACL 2022|

|156|[CHQ-Summ](https://github.com/shwetanlp/Yahoo-CHQ-Summ)||[CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization](https://arxiv.org/abs/2206.06581)||

|157|[Multi-LexSum](https://github.com/multilexsum/dataset)|multi-doc|[Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities](https://arxiv.org/abs/2206.10883)||

|158|[DACSA](https://xarrador.dsic.upv.es/resources/dacsa)|Catalan and Spanish|[DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles](https://aclanthology.org/2022.naacl-main.434/)|NAACL 2022|

|159|[BigSurvey](https://github.com/StevenLau6/BigSurvey)|Academic Multi-doc|[Generating a Structured Summary of Numerous Academic Papers: Dataset and Method](https://www.ijcai.org/proceedings/2022/0591.pdf)|IJCAI 2022|

|160|[CSL](https://github.com/ydli-ai/CSL)![](https://img.shields.io/badge/-Chinese-orange)|Chinese, Academic|[CSL: A Large-scale Chinese Scientific Literature Dataset](https://arxiv.org/abs/2209.05034)|COLING 2022|

|161|[PCC Summaries](https://github.com/fhewett/pcc-summaries)|German|[Extractive Summarisation for German-language Data: A Text-level Approach with Discourse Features](https://aclanthology.org/2022.coling-1.63/)|COLING 2022|

|162|[LipKey](https://github.com/fajri91/LipKey)|abstractive summaries, absent keyphrases, and titles|[LipKey: A Large-Scale News Dataset for Absent Keyphrases Generation and Abstractive Summarization](https://aclanthology.org/2022.coling-1.303/)|COLING 2022|

|163|[PLOS](https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation)|Lay summary of biomedical journal articles|[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)|EMNLP 2022|

|164|[eLife](https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation)|Lay summary of biomedical journal articles|[Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)|EMNLP 2022|

|165|[ECTSum](https://github.com/rajdeep345/ECTSum)|Long Earnings Call Transcripts|[ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts](https://arxiv.org/abs/2210.12467)|EMNLP 2022|

|166|[EUR-Lex-Sum](https://github.com/achouhan93/eur-lex-sum)|Multi- and Cross-lingual Legal|[EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain](https://arxiv.org/abs/2210.13448)|EMNLP 2022|

|167|[CrisisLTLSum](https://github.com/CrisisLTLSum/CrisisTimelines)|Timeline|[CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization](https://arxiv.org/abs/2210.14190)||

|168|LANS(`upon request`)|Arabic|[LANS: Large-scale Arabic News Summarization Corpus](https://arxiv.org/abs/2210.13600)||

|169|[MACSUM](https://github.com/psunlpgroup/MACSum)|Controllable News Dialogue|[MACSUM: Controllable Summarization with Mixed Attributes](https://arxiv.org/abs/2211.05041)||

|170|[NarraSum](https://github.com/zhaochaocs/narrasum)|Narrative|[NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://arxiv.org/abs/2212.01476)|EMNLP Findings 2022|

|171|[LoRaLay](https://github.com/recitalAI/loralay-datasets)|Long Scientific Visual|[LoRaLay: A Multilingual and Multimodal Dataset for Long Range and Layout-Aware Summarization](https://arxiv.org/abs/2301.11312)|EACL 2023|

|172|[HunSum-1](https://github.com/dorinapetra/summarization)|Hungarian|[HunSum-1: an Abstractive Summarization Dataset for Hungarian](https://arxiv.org/abs/2302.00455)||

|173|[MCLS](https://github.com/korokes/MCLS)|ultimodal Cross-Lingual|[Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos](https://aclanthology.org/2022.emnlp-main.468/)|EMNLP 2022|

|174|[JDDC 2.1](https://github.com/hrlinlp/jddc2.1)|multimodal|[JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization](https://aclanthology.org/2022.emnlp-main.825/)|EMNLP 2022|

|175|[CroCoSum](https://github.com/RosenZhang/CroCoSum)|Code-switched Cross-lingual|[CroCoSum: A Benchmark Dataset for Cross-Lingual Code-Switched Summarization](https://arxiv.org/abs/2303.04092)||

|176|[unarXive](https://github.com/IllDepence/unarXive)|scholarly|[unarXive: a large scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata](https://link.springer.com/article/10.1007/s11192-020-03382-z)|Scientometrics 2020|

|177|[TempoSum](https://github.com/AndyCheang/TempoSum)||[TempoSum: Evaluating the Temporal Generalization of Abstractive Summarization](https://arxiv.org/abs/2305.01951)||

|178|[VCSUM](https://github.com/hahahawu/VCSum)|meeting|[VCSUM: A Versatile Chinese Meeting Summarization Dataset](https://arxiv.org/abs/2305.05280)|ACL Findings 2023|

|179|[MeetingBank](https://meetingbank.github.io/)|meeting|[MeetingBank: A Benchmark Dataset for Meeting Summarization](https://aclanthology.org/2023.acl-long.906/)|ACL 2023|

## Dialogue 

### Dataset

1. **MeetingBank: A Benchmark Dataset for Meeting Summarization** *Yebowen Hu, Timothy Ganter, Hanieh Deilamsalehy, Franck Dernoncourt, Hassan Foroosh, Fei Liu* `ACL 2023` [[pdf]](https://aclanthology.org/2023.acl-long.906/) [[data]](https://meetingbank.github.io/)   [Abs] As the number of recorded meetings increases, it becomes increasingly important to utilize summarization technology to create useful summaries of these recordings. However, there is a crucial lack of annotated meeting corpora for developing this technology, as it can be hard to collect meetings, especially when the topics discussed are confidential. Furthermore, meeting summaries written by experienced writers are scarce, making it hard for abstractive summarizers to produce sensible output without a reliable reference. This lack of annotated corpora has hindered the development of meeting summarization technology. In this paper, we present MeetingBank, a new benchmark dataset of city council meetings over the past decade. MeetingBank is unique among other meeting corpora due to its divide-and-conquer approach, which involves dividing professionally written meeting minutes into shorter passages and aligning them with specific segments of the meeting. This breaks down the process of summarizing a lengthy meeting into smaller, more manageable tasks. The dataset provides a new testbed of various meeting summarization systems and also allows the public to gain insight into how council decisions are made. We make the collection, including meeting video links, transcripts, reference summaries, agenda, and other metadata, publicly available to facilitate the development of better meeting summarization techniques. 

1. **ECTSum: A New Benchmark Dataset For Bullet Point Summarization of Long Earnings Call Transcripts** *Rajdeep Mukherjee, Abhinav Bohra, Akash Banerjee, Soumya Sharma, Manjunath Hegde, Afreen Shaikh, Shivani Shrivastava, Koustuv Dasgupta, Niloy Ganguly, Saptarshi Ghosh, Pawan Goyal* `EMNLP 2022` [[pdf]](https://arxiv.org/abs/2210.12467) [[data]](https://github.com/rajdeep345/ECTSum)  [Abs] Despite tremendous progress in automatic summarization, state-of-the-art methods are predominantly trained to excel in summarizing short newswire articles, or documents with strong layout biases such as scientific articles or government reports. Efficient techniques to summarize financial documents, including facts and figures, have largely been unexplored, majorly due to the unavailability of suitable datasets. In this work, we present ECTSum, a new dataset with transcripts of earnings calls (ECTs), hosted by publicly traded companies, as documents, and short experts-written telegram-style bullet point summaries derived from corresponding Reuters articles. ECTs are long unstructured documents without any prescribed length limit or format. We benchmark our dataset with state-of-the-art summarizers across various metrics evaluating the content quality and factual consistency of the generated summaries. Finally, we present a simple-yet-effective approach, ECT-BPS, to generate a set of bullet points that precisely capture the important facts discussed in the calls.

1. **TODSum: Task-Oriented Dialogue Summarization with State Tracking** *Lulu Zhao, Fujia Zheng, Keqing He, Weihao Zeng, Yuejie Lei, Huixing Jiang, Wei Wu, Weiran Xu, Jun Guo, Fanyu Meng* [[pdf]](https://arxiv.org/abs/2110.12680)

2. **TWEETSUMM - A Dialog Summarization Dataset for Customer Service** *Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov* `Findings of EMNLP 2021` [[pdf]](https://aclanthology.org/2021.findings-emnlp.24/) [[data]](https://github.com/guyfe/Tweetsumm)

3. **ForumSum: A Multi-Speaker Conversation Summarization Dataset** *Misha Khalman, Yao Zhao, Mohammad Saleh* `EMNLP 2021 Findings` [[pdf]](https://aclanthology.org/2021.findings-emnlp.391/) [[data]](https://huggingface.co/datasets/forumsum)

4. **CSDS: A Fine-grained Chinese Dataset for Customer Service Dialogue Summarization** *Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong* `EMNLP 2021` [[pdf]](https://aclanthology.org/2021.emnlp-main.365/) [[data]](https://github.com/xiaolinAndy/CSDS)

5. **EmailSum: Abstractive Email Thread Summarization** *Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao, Mohit Bansal* `ACL 2021` [[pdf]](https://aclanthology.org/2021.acl-long.537/) [[data]](https://github.com/ZhangShiyue/EmailSum)

6. **DialSumm: A Real-Life Scenario Dialogue Summarization Dataset** *Yulong Chen, Yang Liu, Liang Chen, Yue Zhang* `Findings of ACL21` [[pdf]](https://arxiv.org/abs/2105.06762) [[data]](https://github.com/cylnlp/DialSumm)

7. **ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining** *Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi, Borui Wang, Haoran Li, Yashar Mehdad, Dragomir Radev* `ACL 2021` [[pdf]](https://aclanthology.org/2021.acl-long.535/) [[code]](https://github.com/Yale-LILY/ConvoSumm)

8. **MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization** *Chenguang Zhu, Yang Liu, Jie Mei, Michael Zeng* `NAACL21` [[pdf]](https://arxiv.org/abs/2103.06410) [[code]](https://github.com/zcgzcgzcg1/MediaSum) 

9. **QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization** *Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev* `NAACL21` [[pdf]](https://arxiv.org/abs/2104.05938) [[data]](https://github.com/Yale-LILY/QMSum) 

10. **Storytelling with Dialogue: A Critical Role Dungeons and Dragons Dataset** *Revanth Rameshkumar, Peter Bailey* `ACL20` [[pdf]](https://www.aclweb.org/anthology/2020.acl-main.459/) [[data]](https://github.com/RevanthRameshkumar/CRD3) 

11. **SumTitles: a Summarization Dataset with Low Extractiveness** *Valentin Malykh, Konstantin Chernis, Ekaterina Artemova, Irina Piontkovskaya* `COLING20` [[pdf]](https://www.aclweb.org/anthology/2020.coling-main.503/) [[code]](https://github.com/huawei-noah/sumtitles) 

12. **Summarizing Medical Conversations via Identifying Important Utterances** *Yan Song, Yuanhe Tian, Nan Wang, Fei Xia* `COLING20` [[pdf]](https://www.aclweb.org/anthology/2020.coling-main.63/) [[code]](https://github.com/cuhksz-nlp/HET-MC) 

13. **GupShup: Summarizing Open-Domain Code-Switched Conversations** *Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, Rajiv Ratn Shah* `EMNLP 2021` [[pdf]](https://aclanthology.org/2021.emnlp-main.499/)[[code]](https://github.com/midas-research/gupshup)

14. **SummScreen: A Dataset for Abstractive Screenplay Summarization** *Mingda Chen, Zewei Chu, Sam Wiseman, Kevin Gimpel* `ACL 2022` [[pdf]](https://aclanthology.org/2022.acl-long.589/) [[data]](https://github.com/mingdachen/SummScreen)  [Abs] We introduce SummScreen, a summarization dataset comprised of pairs of TV series transcripts and human written recaps. The dataset provides a challenging testbed for abstractive summarization for several reasons. Plot details are often expressed indirectly in character dialogues and may be scattered across the entirety of the transcript. These details must be found and integrated to form the succinct plot descriptions in the recaps. Also, TV scripts contain content that does not directly pertain to the central plot but rather serves to develop characters or provide comic relief. This information is rarely contained in recaps. Since characters are fundamental to TV series, we also propose two entity-centric evaluation metrics. Empirically, we characterize the dataset by evaluating several methods, including neural models and those based on nearest neighbors. An oracle extractive approach outperforms all benchmarked models according to automatic metrics, showing that the neural models are unable to fully exploit the input transcripts. Human evaluation and qualitative analysis reveal that our non-oracle models are competitive with their oracle counterparts in terms of generating faithful plot events and can benefit from better content selectors. Both oracle and non-oracle models generate unfaithful facts, suggesting future research directions. 

15. **SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization** *Bogdan Gliwa, Iwona Mochol, Maciej Biesek, Aleksander Wawer* `EMNLP19` [[pdf]](https://arxiv.org/abs/1911.12237) [[data]](https://arxiv.org/src/1911.12237v2/anc/corpus.7z) 

16. **Dial2Desc: End-to-end Dialogue Description Generation** *Haojie Pan, Junpei Zhou, Zhou Zhao, Yan Liu, Deng Cai, Min Yang* [[pdf]](https://arxiv.org/abs/1811.00185) 

17. **The AMI meeting corpus: A pre-announcement** *Carletta, Jean and Ashby, Simone and Bourban, Sebastien and Flynn, Mike and Guillemot, Mael and Hain, Thomas and Kadlec, Jaroslav and Karaiskos, Vasilis and Kraaij, Wessel and Kronenthal, Melissa and others* [[pdf]](https://link.springer.com/chapter/10.1007/11677482_3)

18. **The ICSI meeting corpus** *Janin, Adam and Baron, Don and Edwards, Jane and Ellis, Dan and Gelbart, David and Morgan, Nelson and Peskin, Barbara and Pfau, Thilo and Shriberg, Elizabeth and Stolcke, Andreas and others* [[pdf]](https://www.researchgate.net/publication/4015071_The_ICSI_meeting_corpus)

### Email Summarization

1. **Focus on the Action: Learning to Highlight and Summarize Jointly for Email To-Do Items Summarization** *Kexun Zhang, Jiaao Chen, Diyi Yang* `Findings of ACL 2022` [[pdf]](https://faculty.cc.gatech.edu/~dyang888/docs/acl22_summarization.pdf)

1. **EmailSum: Abstractive Email Thread Summarization** *Shiyue Zhang, Asli Celikyilmaz, Jianfeng Gao, Mohit Bansal* `ACL 2021` [[pdf]](https://aclanthology.org/2021.acl-long.537/) [[data]](https://github.com/ZhangShiyue/EmailSum)

2. **Smart To-Do: Automatic Generation of To-Do Items from Emails** *Sudipto Mukherjee, Subhabrata Mukherjee, Marcello Hasegawa, Ahmed Hassan Awadallah, Ryen White* `ACL 2020` [[pdf]](https://www.aclweb.org/anthology/2020.acl-main.767/) [[code]](https://github.com/MSR-LIT/SmartToDo) [[bib]](https://www.aclweb.org/anthology/2020.acl-main.767.bib)

3. **Identifying Implicit Quotes for Unsupervised Extractive Summarization of Conversations** *Ryuji Kano, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma* `AACL20` [[pdf]](https://www.aclweb.org/anthology/2020.aacl-main.32/) 

4. **This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation** *Rui Zhang, Joel Tetreault* `ACL 2019` [[pdf]](https://www.aclweb.org/anthology/P19-1043/) [[data]](https://github.com/ryanzhumich/AESLC) [[bib]](https://www.aclweb.org/anthology/P19-1043.bib)

5. **Building a Dataset for Summarization and Keyword Extraction from Emails** *Vanessa Loza, Shibamouli Lahiri, Rada Mihalcea, Po-Hsiang Lai* `LREC 2014` [[pdf]](https://www.aclweb.org/anthology/L14-1028/)

6. **A Publicly Available Annotated Corpus for Supervised Email Summarization** *Jan Ulrich, Gabriel Murray, Giuseppe Carenini* `AAAI 2008` [[pdf]](https://www.aaai.org/Papers/Workshops/2008/WS-08-04/WS08-04-014.pdf)

7. **Summarizing Email Conversations with Clue Words** *Giuseppe Carenini, Raymond T. Ng, Xiaodong Zhou* `WWW 2007` [[pdf]](https://www2007.org/papers/paper631.pdf)

8. **Task-focused Summarization of Email** *Simon H. Corston-Oliver Eric Ringger Michael Gamon Richard Campbell* `ACL 2004` [[pdf]](https://www.aclweb.org/anthology/W04-1008.pdf)

9. **Summarizing email threads** *Owen Rambow, Lokesh Shrestha, John Chen, Chirsty Lauridsen* `NAACL 2004` [[pdf]](https://www.aclweb.org/anthology/N04-4027/) [[bib]](https://www.aclweb.org/anthology/N04-4027.bib)

10. **Facilitating email thread access by extractive summary generation** *Ani Nenkova* `Recent advances in natural language pr
ecosyste.ms

Data

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