{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00215076","sets":["6504:10735:10808"]},"path":["10808"],"owner":"44499","recid":"215076","title":["文書表現モデルsent2vecを用いた抽出的要約の生成手法"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-04"},"_buckets":{"deposit":"03d15e75-9bb1-40b5-bdac-366ea8790557"},"_deposit":{"id":"215076","pid":{"type":"depid","value":"215076","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"文書表現モデルsent2vecを用いた抽出的要約の生成手法","author_link":["553575","553576"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"文書表現モデルsent2vecを用いた抽出的要約の生成手法"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2021-03-04","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"法大"},{"subitem_text_value":"法大"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/215076/files/IPSJ-Z83-2S-07.pdf","label":"IPSJ-Z83-2S-07.pdf"},"date":[{"dateType":"Available","dateValue":"2021-12-28"}],"format":"application/pdf","filename":"IPSJ-Z83-2S-07.pdf","filesize":[{"value":"337.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"a70d64d0-48b5-43db-a9ae-75aeb8540795","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"門木, 斗夢"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤田, 悟"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年、自然言語処理の分野では、与えられた文書を自動で要約するタスクが話題となっている。一方で、深層学習を用いて文の意味を分析するために、文や文書を特徴ベクトルとして表現するsent2vecが提案されている。本研究では、与えられた文書を文単位でsent2vecを用いて変換し、それを束ねた行列表現に対し、PCAやICAを適用することで、トピックを表現するコンテキストベクトルを取得する手法を明らかにした。さらに、このコンテキストベクトルを用いて、文書を意味的に分類することや、文書の要約となる重要文の抽出を行う手法を提案する。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"556","bibliographic_titles":[{"bibliographic_title":"第83回全国大会講演論文集"}],"bibliographicPageStart":"555","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2021"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":215076,"updated":"2025-01-19T16:19:58.422441+00:00","links":{},"created":"2025-01-19T01:15:51.530346+00:00"}