PERSONALIZED EXTRACTIVE SUMMARIZATION FOR A NEWS DIALOGUE SYSTEM

被引：0
|
作者：
Takatsu, Hiroaki [1 ]
Okuda, Mayu [1 ]
Matsuyama, Yoichi [1 ]
Honda, Hiroshi [2 ]
Fujie, Shinya [3 ]
Kobayashi, Tetsunori [1 ]
机构：
[1] Waseda Univ, Tokyo, Japan
[2] Honda Motor Co Ltd, Tokyo, Japan
[3] Chiba Inst Technol, Chiba, Japan
来源：
2021 IEEE SPOKEN LANGUAGE TECHNOLOGY WORKSHOP (SLT) | 2021年
基金：
日本科学技术振兴机构;
关键词：
automatic text summarization; personalization; spoken dialogue system; MODEL;
D O I：
10.1109/SLT48900.2021.9383568
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：1044 / 1051
页数：8
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