{"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/an-editorial-network-for-enhanced-document","title":"An Editorial Network for Enhanced Document Summarization","arxiv_id":"1902.10360","date":"2019-02-27","proceeding":"WS 2019 11","authors":["Edward Moroshko","Guy Feigenblat","Haggai Roitman","David Konopnicki"],"abstract":"We suggest a new idea of Editorial Network - a mixed extractive-abstractive\nsummarization approach, which is applied as a post-processing step over a given\nsequence of extracted sentences. Our network tries to imitate the decision\nprocess of a human editor during summarization. Within such a process, each\nextracted sentence may be either kept untouched, rephrased or completely\nrejected. We further suggest an effective way for training the \"editor\" based\non a novel soft-labeling approach. Using the CNN/DailyMail dataset we\ndemonstrate the effectiveness of our approach compared to state-of-the-art\nextractive-only or abstractive-only baseline methods.","url_abs":"http://arxiv.org/abs/1902.10360v1","url_pdf":"http://arxiv.org/pdf/1902.10360v1.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":[],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abstractive-text-summarization-on-cnn-daily","task":"Abstractive Text Summarization","dataset":"CNN / Daily Mail","model":"EditNet","rank_in_archive_order":34,"of":53,"metrics":{"ROUGE-1":"41.42","ROUGE-2":"19.03","ROUGE-L":"38.36"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}