{"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/deep-recurrent-generative-decoder-for","title":"Deep Recurrent Generative Decoder for Abstractive Text Summarization","arxiv_id":"1708.00625","date":"2017-08-02","proceeding":"EMNLP 2017 9","authors":["Piji Li","Wai Lam","Lidong Bing","ZiHao Wang"],"abstract":"We propose a new framework for abstractive text summarization based on a\nsequence-to-sequence oriented encoder-decoder model equipped with a deep\nrecurrent generative decoder (DRGN).\n  Latent structure information implied in the target summaries is learned based\non a recurrent latent random model for improving the summarization quality.\n  Neural variational inference is employed to address the intractable posterior\ninference for the recurrent latent variables.\n  Abstractive summaries are generated based on both the generative latent\nvariables and the discriminative deterministic states.\n  Extensive experiments on some benchmark datasets in different languages show\nthat DRGN achieves improvements over the state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1708.00625v1","url_pdf":"http://arxiv.org/pdf/1708.00625v1.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":"deep-recurrent-generative-decoder-for","repo_url":"https://github.com/toru34/li_emnlp_2017","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"text-summarization","task_name":"Text Summarization"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-summarization-on-duc-2004-task-1","task":"Text Summarization","dataset":"DUC 2004 Task 1","model":"DRGD","rank_in_archive_order":5,"of":13,"metrics":{"ROUGE-1":"31.79","ROUGE-2":"10.75","ROUGE-L":"27.48"},"uses_additional_data":false},{"leaderboard":"/sota/text-summarization-on-gigaword","task":"Text Summarization","dataset":"GigaWord","model":"DRGD","rank_in_archive_order":33,"of":41,"metrics":{"ROUGE-1":"36.27","ROUGE-2":"17.57","ROUGE-L":"33.62"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.00625","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}