{"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/structure-infused-copy-mechanisms-for","title":"Structure-Infused Copy Mechanisms for Abstractive Summarization","arxiv_id":"1806.05658","date":"2018-06-14","proceeding":"COLING 2018 8","authors":["Kaiqiang Song","Lin Zhao","Fei Liu"],"abstract":"Seq2seq learning has produced promising results on summarization. However, in\nmany cases, system summaries still struggle to keep the meaning of the original\nintact. They may miss out important words or relations that play critical roles\nin the syntactic structure of source sentences. In this paper, we present\nstructure-infused copy mechanisms to facilitate copying important words and\nrelations from the source sentence to summary sentence. The approach naturally\ncombines source dependency structure with the copy mechanism of an abstractive\nsentence summarizer. Experimental results demonstrate the effectiveness of\nincorporating source-side syntactic information in the system, and our proposed\napproach compares favorably to state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1806.05658v2","url_pdf":"http://arxiv.org/pdf/1806.05658v2.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":"structure-infused-copy-mechanisms-for","repo_url":"https://github.com/KaiQiangSong/struct_infused_summ","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"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/text-summarization-on-gigaword","task":"Text Summarization","dataset":"GigaWord","model":"Struct+2Way+Word","rank_in_archive_order":36,"of":41,"metrics":{"ROUGE-1":"35.47","ROUGE-2":"17.66","ROUGE-L":"33.52"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.05658","atlas_url":"https://app.syntology.ai/?focus=1806.05658","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}