{"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/cutting-off-redundant-repeating-generations","title":"Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization","arxiv_id":"1701.00138","date":"2016-12-31","proceeding":"EACL 2017 4","authors":["Jun Suzuki","Masaaki Nagata"],"abstract":"This paper tackles the reduction of redundant repeating generation that is\noften observed in RNN-based encoder-decoder models. Our basic idea is to\njointly estimate the upper-bound frequency of each target vocabulary in the\nencoder and control the output words based on the estimation in the decoder.\nOur method shows significant improvement over a strong RNN-based\nencoder-decoder baseline and achieved its best results on an abstractive\nsummarization benchmark.","url_abs":"http://arxiv.org/abs/1701.00138v2","url_pdf":"http://arxiv.org/pdf/1701.00138v2.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":"decoder","task_name":"Decoder"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-summarization-on-duc-2004-task-1","task":"Text Summarization","dataset":"DUC 2004 Task 1","model":"EndDec+WFE","rank_in_archive_order":4,"of":13,"metrics":{"ROUGE-1":"32.28","ROUGE-2":"10.54","ROUGE-L":"27.8"},"uses_additional_data":false},{"leaderboard":"/sota/text-summarization-on-gigaword","task":"Text Summarization","dataset":"GigaWord","model":"EndDec+WFE","rank_in_archive_order":32,"of":41,"metrics":{"ROUGE-1":"36.30","ROUGE-2":"17.31","ROUGE-L":"33.88"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.00138","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}