{"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/a-unified-model-for-extractive-and","title":"A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss","arxiv_id":"1805.06266","date":"2018-05-16","proceeding":"ACL 2018 7","authors":["Wan-Ting Hsu","Chieh-Kai Lin","Ming-Ying Lee","Kerui Min","Jing Tang","Min Sun"],"abstract":"We propose a unified model combining the strength of extractive and\nabstractive summarization. On the one hand, a simple extractive model can\nobtain sentence-level attention with high ROUGE scores but less readable. On\nthe other hand, a more complicated abstractive model can obtain word-level\ndynamic attention to generate a more readable paragraph. In our model,\nsentence-level attention is used to modulate the word-level attention such that\nwords in less attended sentences are less likely to be generated. Moreover, a\nnovel inconsistency loss function is introduced to penalize the inconsistency\nbetween two levels of attentions. By end-to-end training our model with the\ninconsistency loss and original losses of extractive and abstractive models, we\nachieve state-of-the-art ROUGE scores while being the most informative and\nreadable summarization on the CNN/Daily Mail dataset in a solid human\nevaluation.","url_abs":"http://arxiv.org/abs/1805.06266v2","url_pdf":"http://arxiv.org/pdf/1805.06266v2.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":"a-unified-model-for-extractive-and","repo_url":"https://github.com/HsuWanTing/unified-summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text 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":"end2end w/ inconsistency loss","rank_in_archive_order":39,"of":53,"metrics":{"ROUGE-1":"40.68","ROUGE-2":"17.97","ROUGE-L":"37.13"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.06266","atlas_url":"https://app.syntology.ai/?focus=1805.06266","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.06266"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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