{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/jointly-extracting-and-compressing","title":"Jointly Extracting and Compressing Documents with Summary State Representations","arxiv_id":"1904.02020","date":"2019-04-03","proceeding":"NAACL 2019 6","authors":["Afonso Mendes","Shashi Narayan","Sebastião Miranda","Zita Marinho","André F. T. Martins","Shay B. Cohen"],"abstract":"We present a new neural model for text summarization that first extracts\nsentences from a document and then compresses them. The proposed model offers a\nbalance that sidesteps the difficulties in abstractive methods while generating\nmore concise summaries than extractive methods. In addition, our model\ndynamically determines the length of the output summary based on the gold\nsummaries it observes during training and does not require length constraints\ntypical to extractive summarization. The model achieves state-of-the-art\nresults on the CNN/DailyMail and Newsroom datasets, improving over current\nextractive and abstractive methods. Human evaluations demonstrate that our\nmodel generates concise and informative summaries. We also make available a new\ndataset of oracle compressive summaries derived automatically from the\nCNN/DailyMail reference summaries.","url_abs":"http://arxiv.org/abs/1904.02020v2","url_pdf":"http://arxiv.org/pdf/1904.02020v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"jointly-extracting-and-compressing","repo_url":"https://github.com/Priberam/exconsumm","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"extractive-summarization","task_name":"Extractive Summarization"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.02020","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}