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https://github.com/guan-yuan/awesome-Multi-Document-Summarization

A curated list of Multi-Document Summarization papers, articles, tutorials, slides , datasets, and projects
https://github.com/guan-yuan/awesome-Multi-Document-Summarization

List: awesome-Multi-Document-Summarization

deep-learning multi-document-summarization pytorch summarisation tensorflow

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A curated list of Multi-Document Summarization papers, articles, tutorials, slides , datasets, and projects

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# awesome-Multi-Document-Summarization

A curated list of Multi-Document Summarization papers, articles, tutorials, slides , datasets, and projects.



## Papers

### Multi-Document-Summarization

#### Supervised

- [Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization](https://www.aclweb.org/anthology/D19-5404/) | [**EMNLP 2019**]



- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://arxiv.org/abs/1906.01749) | [**ACL 2019**]

  + [Alex-Fabbri/Multi-News](https://github.com/Alex-Fabbri/Multi-News) | [**pytorch**]



- [Hierarchical Transformers for Multi-Document Summarization](https://arxiv.org/abs/1905.13164) | [**ACL 2019**]

  + [nlpyang/hiersumm](https://github.com/nlpyang/hiersumm) | [**pytorch**]



- [Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization](https://arxiv.org/abs/1906.00072) | [**ACL 2019**]

  + [ucfnlp/summarization-dpp-capsnet](https://github.com/ucfnlp/summarization-dpp-capsnet) | [**keras**]



- [Fast Concept Mention Grouping for Concept Map-based Multi-Document Summarization](https://www.aclweb.org/anthology/N19-1074/) | [**NAACL 2019**]

  + [UKPLab/naacl2019-cmaps-lshcw](https://github.com/UKPLab/naacl2019-cmaps-lshcw) | [**java**]



#### Unsupervised

- [Unsupervised Aspect-Based Multi-Document Abstractive Summarization](https://www.aclweb.org/anthology/D19-5405/) | [**EMNLP 2019**]



- [MeanSum: A Neural Model for Unsupervised Multi-document Abstractive Summarization](https://arxiv.org/abs/1810.05739)  | [**ICML 2019**]

  + [sosuperic/MeanSum](https://github.com/sosuperic/MeanSum) [**pytorch**]



- [Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion](https://www.aclweb.org/anthology/C18-1102/) | [**COLING 2018**]



- [Unsupervised Semantic Abstractive Summarization](https://aclweb.org/anthology/P18-3011/) | [**ACL 2018**]



- [Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14613) | [**AAAI 2017**]



- [An Unsupervised Multi-Document Summarization Framework Based on Neural Document Model](https://www.aclweb.org/anthology/C16-1143/) | [**COLING 2016**]



#### Datasets

- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://arxiv.org/abs/1906.01749) | [**ACL 2019**]

  + [Alex-Fabbri/Multi-News](https://github.com/Alex-Fabbri/Multi-News) | [**pytorch**]



---



### Single-Document-Summarization (as references)

#### Supervised

- [An Entity-Driven Framework for Abstractive Summarization](https://www.aclweb.org/anthology/D19-1323/) | [**EMNLP 2019**]



- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://arxiv.org/abs/1905.03197) | [**NeurIPS 2019**]

  + [microsoft/unilm](https://github.com/microsoft/unilm)



- [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://arxiv.org/abs/1909.03186) | [2019/09]



- [Fine-tune BERT for Extractive Summarization](https://arxiv.org/abs/1903.10318) | [2019/09]

  + [nlpyang/BertSum](https://github.com/nlpyang/BertSum)



- [Guiding Extractive Summarization with Question-Answering Rewards](https://arxiv.org/abs/1904.02321) | [**NAACL 2019**]



- [Abstractive Text Summarization by Incorporating Reader Comments](https://arxiv.org/abs/1812.05407) | [**AAAI 2019**]



- [Structured Neural Summarization](https://arxiv.org/abs/1811.01824) | [**ICLR 2019**]



- [A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss](https://arxiv.org/abs/1805.06266) | [**ACL 2018**]



- [Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization](https://www.aclweb.org/anthology/P18-2115/) | [**ACL 2018**]

  + [lancopku/superAE](https://github.com/lancopku/superAE)



- [Generating Topic-Oriented Summaries Using Neural Attention](https://www.aclweb.org/anthology/N18-1153/) | [**NAACL 2018**]



- [Abstractive Document Summarization with a Graph-Based Attentional Neural Model](https://www.aclweb.org/anthology/P17-1108/) | [**ACL 2017**]



#### Unsupervised

- [SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders](https://arxiv.org/abs/1910.00998) | [2019/10]



- [BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle](https://arxiv.org/abs/1909.07405) | [**EMNLP 2019**]



- [Sentence Centrality Revisited for Unsupervised Summarization](https://arxiv.org/abs/1906.03508) | [**ACL 2019**]

  + [mswellhao/PacSum](https://github.com/mswellhao/PacSum)



- [Simple Unsupervised Summarization by Contextual Matching](https://arxiv.org/abs/1907.13337) | [**ACL 2019**]



- [SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression](https://www.aclweb.org/anthology/N19-1071/) | [**NAACL 2019**]



- [Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks](https://arxiv.org/abs/1810.02851) | [**EMNLP 2018**]



- [Unsupervised Text Summarization using Sentence Embeddings](https://medium.com/jatana/unsupervised-text-summarization-using-sentence-embeddings-adb15ce83db1) | [2018/08]

  + [jatana-research/email-summarization](https://github.com/jatana-research/email-summarization)



- [isnowfy/snownlp](https://github.com/isnowfy/snownlp)



#### Datasets



#### Chinese Datasets

- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://www.aclweb.org/anthology/D15-1229/)



- [NLPCC 2017 Shared Task](http://tcci.ccf.org.cn/conference/2017/taskdata.php)

  + [yangzhiye/NLPCC2017-task3](https://github.com/yangzhiye/NLPCC2017-task3)



- [Overview of the NLPCC 2018 Shared Task: Single Document Summarization](http://tcci.ccf.org.cn/conference/2018/papers/EV48.pdf)



- [Overview of the NLPCC 2017 Shared Task: Single Document Summarization](http://59.108.48.5/lcwm/wanxj/files/NLPCC2017-Overview.pdf)



---



## Sentence Embeddings

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084) 

 [**EMNLP 2019**]

  + [UKPLab/sentence-transformers](https://github.com/UKPLab/sentence-transformers)



- [Semantic Similarity in Sentences and BERT](https://medium.com/analytics-vidhya/semantic-similarity-in-sentences-and-bert-e8d34f5a4677) | [**2019/09**]



---



## Metrics

 + [pltrdy/rouge](https://github.com/pltrdy/rouge)

 

 - [Neural Text Summarization: A Critical Evaluation](https://arxiv.org/abs/1908.08960) | [**EMNLP 2019**]

 

 - [Answers Unite! Unsupervised Metrics for Reinforced Summarization Models](https://arxiv.org/abs/1909.01610) | [**EMNLP 2019**]



---



## References

 - [mathsyouth/awesome-text-summarization](https://github.com/mathsyouth/awesome-text-summarization)

 - [luopeixiang/awesome-text-summarization](https://github.com/luopeixiang/awesome-text-summarization)

 - [ChineseNLP](https://chinesenlp.xyz/zh/docs/text_summarization.html)