TY - GEN
T1 - Personalized Extractive Summarization for a News Dialogue System
AU - Takatsu, Hiroaki
AU - Okuda, Mayu
AU - Matsuyama, Yoichi
AU - Honda, Hiroshi
AU - Fujie, Shinya
AU - Kobayashi, Tetsunori
N1 - Funding Information:
This work was supported by Japan Science and Technology Agency (JST) Program for Creating STart-ups from Advanced Research and Technology (START), Grant Number JPMJST1912 “Commercialization of Socially-Intelligent Conversational AI Media Service”.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/19
Y1 - 2021/1/19
N2 - 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.
AB - 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.
KW - automatic text summarization
KW - personalization
KW - spoken dialogue system
UR - http://www.scopus.com/inward/record.url?scp=85103952111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103952111&partnerID=8YFLogxK
U2 - 10.1109/SLT48900.2021.9383568
DO - 10.1109/SLT48900.2021.9383568
M3 - Conference contribution
AN - SCOPUS:85103952111
T3 - 2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
SP - 1044
EP - 1051
BT - 2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Spoken Language Technology Workshop, SLT 2021
Y2 - 19 January 2021 through 22 January 2021
ER -