{"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/neural-document-summarization-by-jointly","title":"Neural Document Summarization by Jointly Learning to Score and Select Sentences","arxiv_id":"1807.02305","date":"2018-07-06","proceeding":"ACL 2018 7","authors":["Qingyu Zhou","Nan Yang","Furu Wei","Shaohan Huang","Ming Zhou","Tiejun Zhao"],"abstract":"Sentence scoring and sentence selection are two main steps in extractive\ndocument summarization systems. However, previous works treat them as two\nseparated subtasks. In this paper, we present a novel end-to-end neural network\nframework for extractive document summarization by jointly learning to score\nand select sentences. It first reads the document sentences with a hierarchical\nencoder to obtain the representation of sentences. Then it builds the output\nsummary by extracting sentences one by one. Different from previous methods,\nour approach integrates the selection strategy into the scoring model, which\ndirectly predicts the relative importance given previously selected sentences.\nExperiments on the CNN/Daily Mail dataset show that the proposed framework\nsignificantly outperforms the state-of-the-art extractive summarization models.","url_abs":"http://arxiv.org/abs/1807.02305v1","url_pdf":"http://arxiv.org/pdf/1807.02305v1.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":"neural-document-summarization-by-jointly","repo_url":"https://github.com/magic282/NeuSum","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"extractive-document-summarization-1","task_name":"Extractive Document Summarization"},{"task_slug":"extractive-summarization","task_name":"Extractive Summarization"},{"task_slug":"extractive-document-summarization","task_name":"Extractive Text Summarization"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/document-summarization-on-cnn-daily-mail","task":"Document Summarization","dataset":"CNN / Daily Mail","model":"NeuSUM","rank_in_archive_order":15,"of":26,"metrics":{"ROUGE-1":"41.59","ROUGE-2":"19.01","ROUGE-L":"37.98"},"uses_additional_data":false},{"leaderboard":"/sota/extractive-document-summarization-on-cnn","task":"Extractive Text Summarization","dataset":"CNN / Daily Mail","model":"NeuSUM","rank_in_archive_order":10,"of":15,"metrics":{"ROUGE-1":"41.59","ROUGE-2":"19.01","ROUGE-L":"37.98"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.02305","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}