{"id":210632,"created":"2025-01-19T01:11:50.574183+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00210632","sets":["1164:4402:10541:10543"]},"path":["10543"],"owner":"44499","recid":"210632","title":["抽象型要約における教師なしドメイン適応のためのデータ拡張"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-03-22"},"_buckets":{"deposit":"49ea7a65-c2b6-421e-bc04-5dcc749eb103"},"_deposit":{"id":"210632","pid":{"type":"depid","value":"210632","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"抽象型要約における教師なしドメイン適応のためのデータ拡張","author_link":["533692","533693"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"抽象型要約における教師なしドメイン適応のためのデータ拡張"}]},"item_type_id":"4","publish_date":"2021-03-22","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京農工大学工学部情報工学科"},{"subitem_text_value":"東京農工大学大学院工学研究院先端情報科学部門"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Department of Computer and Information Sciences, Faculty of Engineering, Tokyo University of Agriculture and Technology","subitem_text_language":"en"},{"subitem_text_value":"Presently with Division of Advanced Information Technology and Computer Science, Institute of Engineering, Tokyo University of Agriculture and Technology","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/210632/files/IPSJ-ICS21202008.pdf","label":"IPSJ-ICS21202008.pdf"},"date":[{"dateType":"Available","dateValue":"2023-03-22"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-ICS21202008.pdf","filesize":[{"value":"809.8 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"25"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5e9a8b7f-bb3f-49ad-994b-40a101553c42","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"名原, 光洋"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤田, 桂英"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11135936","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-885X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"自然言語処理 (NLP) タスクの 1 つである自動要約タスクでは，他の NLP タスクと同様に大規模なデータによる事前学習を行ったモデルを，小規模なターゲットドメインにドメイン適応させることができる．これにより様々なドメインの文書に対して，自動要約を行うことが可能である．自動要約タスクはその手法によって抽出型と抽象型の 2 つに大別することができ，その中でも抽象型要約は被要約文書の表現に捉われることなく柔軟かつ完成度の高い要約文を生成する．抽象型要約は深層学習に基づいており，その学習には正解データとなる要約が付与された大量のデータが必要になる．このような大規模な抽象要約に適したデータセットはドメインが限られているため，抽象要約が実現できるドメインも限られている．抽象要約で使われるデータセットの 1 つである CNN - Daily Mail コーパス (CNN-DM) はニュース記事とその要約になるいくつかの文からなる見出しがセットになっているデータセットである．本研究ではニュース記事とは性質を大きく異にするレビューサイトに寄せられたレビューの抽象要約に注目する．小規模な教師なしレビューデータセットの抽象要約を実現するために大規模データセットで事前学習したモデルのドメイン適応を前提としたデータ拡張手法を提案する．文脈を考慮した単語の置き換えによるデータ拡張よって，ROUGE において既存手法を上回る精度を確認した．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告知能システム（ICS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-03-22","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"2021-ICS-202"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"updated":"2025-01-19T18:05:12.153907+00:00","links":{}}