{"id":19168394,"url":"https://github.com/bloomberg/entsum","last_synced_at":"2025-05-07T14:41:35.979Z","repository":{"id":39876330,"uuid":"471104315","full_name":"bloomberg/entsum","owner":"bloomberg","description":"Open Source / ENTSUM: A Data Set for Entity-Centric Extractive Summarization","archived":false,"fork":false,"pushed_at":"2022-05-23T21:56:15.000Z","size":28,"stargazers_count":27,"open_issues_count":2,"forks_count":2,"subscribers_count":5,"default_branch":"main","last_synced_at":"2025-04-19T23:31:57.991Z","etag":null,"topics":["nlp"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# EntSUM: A dataset for entity centric summarization\nRepository for pre-processing code related to generating the training datasets used in the [paper](https://aclanthology.org/2022.acl-long.237/).\n\n## Using this repository\nThe repository contains 4 notebooks:\n- [preprocessing_ner_tagging.ipynb](https://bbgithub.dev.bloomberg.com/mkulkarni24/entsum/blob/master/notebooks/preprocessing_ner_tagging.ipynb): This notebook will allow you to tag all the entities using [FLAIR](https://github.com/flairNLP/flair) in the source and summary for both CNN/DailyMail and NYT datasets\n- [preprocessing_coref_resolution.ipynb](https://bbgithub.dev.bloomberg.com/mkulkarni24/entsum/blob/master/notebooks/preprocessing_coref_resolution.ipynb): This notebook takes the entities from the previous entity tagging and performs [Coreference Resolution using SpanBERT](https://github.com/mandarjoshi90/coref) so we don't have duplicate data points for a given entity\n- [preprocessing_bertsum.ipynb](https://bbgithub.dev.bloomberg.com/mkulkarni24/entsum/blob/master/notebooks/preprocessing_bertsum.ipynb): This notebook uses the files generated by the NER tagging and Coreference Resolution to generate the training dataset to be used to [train a BERTSum model](https://github.com/nlpyang/BertSum)\n- [preprocessing_gsum.ipynb](https://bbgithub.dev.bloomberg.com/mkulkarni24/entsum/blob/master/notebooks/preprocessing_gsum.ipynb): This notebook uses the files generated by the NER tagging and Coreference Resolution to generate the training dataset to be used to [train a GSum model](https://github.com/neulab/guided_summarization)\n\n## Datasets\nCNN/DailyMail and NYT are datasets that can be used for training models by setting up entity-centric summarization datasets with methods described in the paper and by leveraging the notebooks mentioned above.\n\n- [CNN/DailyMail](https://cs.nyu.edu/~kcho/DMQA/)\n- [NYT](https://catalog.ldc.upenn.edu/LDC2008T19)\n\nThe EntSUM dataset is used to evaluate the effectiveness of these trained entity-centric summarization models.\n- EntSUM [Zenodo](https://zenodo.org/record/6359875) | [HuggingFace](https://huggingface.co/datasets/bloomberg/entsum)\n\n# License\nThe EntSUM code is distributed under the Apache License (version 2.0); see the LICENSE file at the top of the source tree for more information.\n\n**Note:** To run the code and download the datasets, please obtain the respective licenses for each respectively.\n\n# Citation\n```\n@inproceedings{maddela-etal-2022-entsum,\n    title = \"{E}nt{SUM}: A Data Set for Entity-Centric Extractive Summarization\",\n    author = \"Maddela, Mounica  and\n      Kulkarni, Mayank  and\n      Preotiuc-Pietro, Daniel\",\n    booktitle = \"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = may,\n    year = \"2022\",\n    address = \"Dublin, Ireland\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2022.acl-long.237\",\n    pages = \"3355--3366\",\n    abstract = \"Controllable summarization aims to provide summaries that take into account user-specified aspects and preferences to better assist them with their information need, as opposed to the standard summarization setup which build a single generic summary of a document.We introduce a human-annotated data set EntSUM for controllable summarization with a focus on named entities as the aspects to control.We conduct an extensive quantitative analysis to motivate the task of entity-centric summarization and show that existing methods for controllable summarization fail to generate entity-centric summaries. We propose extensions to state-of-the-art summarization approaches that achieve substantially better results on our data set. Our analysis and results show the challenging nature of this task and of the proposed data set.\",\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbloomberg%2Fentsum","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbloomberg%2Fentsum","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbloomberg%2Fentsum/lists"}