Personalized Extractive Summarization for a News Dialogue System

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1044-1051
Number of pages8
ISBN (Electronic)9781728170664
DOIs
Publication statusPublished - 2021 Jan 19
Event2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Shenzhen, China
Duration: 2021 Jan 192021 Jan 22

Publication series

Name2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings

Conference

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

Keywords

  • automatic text summarization
  • personalization
  • spoken dialogue system

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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