{"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/distraction-based-neural-networks-for","title":"Distraction-Based Neural Networks for Document Summarization","arxiv_id":"1610.08462","date":"2016-10-26","proceeding":null,"authors":["Qian Chen","Xiaodan Zhu","Zhen-Hua Ling","Si Wei","Hui Jiang"],"abstract":"Distributed representation learned with neural networks has recently shown to\nbe effective in modeling natural languages at fine granularities such as words,\nphrases, and even sentences. Whether and how such an approach can be extended\nto help model larger spans of text, e.g., documents, is intriguing, and further\ninvestigation would still be desirable. This paper aims to enhance neural\nnetwork models for such a purpose. A typical problem of document-level modeling\nis automatic summarization, which aims to model documents in order to generate\nsummaries. In this paper, we propose neural models to train computers not just\nto pay attention to specific regions and content of input documents with\nattention models, but also distract them to traverse between different content\nof a document so as to better grasp the overall meaning for summarization.\nWithout engineering any features, we train the models on two large datasets.\nThe models achieve the state-of-the-art performance, and they significantly\nbenefit from the distraction modeling, particularly when input documents are\nlong.","url_abs":"http://arxiv.org/abs/1610.08462v1","url_pdf":"http://arxiv.org/pdf/1610.08462v1.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":"distraction-based-neural-networks-for","repo_url":"https://github.com/lukecq1231/nats","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"document-summarization","task_name":"Document Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1610.08462","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}