Personalized Extractive Summarization for a News Dialogue System

Hiroaki Takatsu, Mayu Okuda, Yoichi Matsuyama, Hiroshi Honda, Shinya Fujie, Tetsunori Kobayashi

研究成果: Conference contribution

抄録

In modern society, people's interests and preferences are diversifying. Along with this, the demand for personalized summarization technology is increasing. In this study, we propose a method for generating summaries tailored to each user's interests using profile features obtained from questionnaires administered to users of our spoken-dialogue news delivery system. We propose a method that collects and uses the obtained user profile features to generate a summary tailored to each user's interests, specifically, the sentence features obtained by BERT and user profile features obtained from the questionnaire result. In addition, we propose a method for extracting sentences by solving an integer linear programming problem that considers redundancy and context coherence, using the degree of interest in sentences estimated by the model. The results of our experiments confirmed that summaries generated based on the degree of interest in sentences estimated using user profile information can transmit information more efficiently than summaries based solely on the importance of sentences.

本文言語English
ホスト出版物のタイトル2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1044-1051
ページ数8
ISBN(電子版)9781728170664
DOI
出版ステータスPublished - 2021 1月 19
イベント2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Shenzhen, China
継続期間: 2021 1月 192021 1月 22

出版物シリーズ

名前2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings

Conference

Conference2021 IEEE Spoken Language Technology Workshop, SLT 2021
国/地域China
CityVirtual, Shenzhen
Period21/1/1921/1/22

ASJC Scopus subject areas

  • 言語学および言語
  • 言語および言語学
  • 人工知能
  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • ハードウェアとアーキテクチャ

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