{"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/extractive-summarization-using-deep-learning","title":"Extractive Summarization using Deep Learning","arxiv_id":"1708.04439","date":"2017-08-15","proceeding":null,"authors":["Sukriti Verma","Vagisha Nidhi"],"abstract":"This paper proposes a text summarization approach for factual reports using a\ndeep learning model. This approach consists of three phases: feature\nextraction, feature enhancement, and summary generation, which work together to\nassimilate core information and generate a coherent, understandable summary. We\nare exploring various features to improve the set of sentences selected for the\nsummary, and are using a Restricted Boltzmann Machine to enhance and abstract\nthose features to improve resultant accuracy without losing any important\ninformation. The sentences are scored based on those enhanced features and an\nextractive summary is constructed. Experimentation carried out on several\narticles demonstrates the effectiveness of the proposed approach. Source code\navailable at: https://github.com/vagisha-nidhi/TextSummarizer","url_abs":"http://arxiv.org/abs/1708.04439v2","url_pdf":"http://arxiv.org/pdf/1708.04439v2.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":"extractive-summarization-using-deep-learning","repo_url":"https://github.com/vagisha-nidhi/TextSummarizer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"extractive-summarization-using-deep-learning","repo_url":"https://github.com/law-ai/summarization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"extractive-summarization","task_name":"Extractive Summarization"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[{"method_slug":"restricted-boltzmann-machine","method_name":"Restricted Boltzmann Machine"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}