Text summarization of dialogue based on BERT (사전 학습된 BERT 기반 모델을 이용한 구어체 텍스트 요약)

Wongyung Nam (남원경); Jisoo Lee (이지수); BEAKCHEOL JANG (장백철)

doi:10.9708/jksci.2022.27.08.041

Text summarization of dialogue based on BERT

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2022, 27(8), pp.41-47
DOI : 10.9708/jksci.2022.27.08.041
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 13, 2022
Accepted : August 8, 2022
Published : August 31, 2022

Wongyung Nam ¹, Jisoo Lee ¹, BEAKCHEOL JANG ¹

¹연세대학교

Accredited

ABSTRACT

In this paper, we propose how to implement text summaries for colloquial data that are not clearly organized. For this study, SAMSum data, which is colloquial data, was used, and the BERTSumExtAbs model proposed in the previous study of the automatic summary model was applied. More than 70% of the SAMSum dataset consists of conversations between two people, and the remaining 30% consists of conversations between three or more people. As a result, by applying the automatic text summarization model to colloquial data, a result of 42.43 or higher was derived in the ROUGE Score R-1. In addition, a high score of 45.81 was derived by fine-tuning the BERTSum model, which was previously proposed as a text summarization model. Through this study, the performance of colloquial generation summary has been proven, and it is hoped that the computer will understand human natural language as it is and be used as basic data to solve various tasks.

KEYWORDS

Text Mining, BERT, Text Summarization, Abstractive Summarization, Dialogue Data

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* References for papers published after 2023 are currently being built.

[other] Liu, Y / 2019 / Fine-tune BERT for extractive summarization

[confproc] Oh, Dongsuk / 2021 / KoDialoGPT2: Modeling Chit-Chat Dialog in Korean / Annual Conference on Human and Language Technology. Human and Language Technology

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[other] ZHOU, Qingyu / 2018 / Neural document summarization by jointly learning to score and select sentences

[confproc] Liu, Y / 2019 / Text summarization with pretrained encoders / EMNLP

[confproc] Feigenblat, G / 2021 / TWEETSUMM--A Dialog Summarization Dataset for Customer Service / EMNLP

[confproc] Bogdan Gliwa / 2019 / SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization / Proceedings of the 2nd Workshop on New Frontiers in Summarization, Association for Computational Linguistics

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[journal] Vaswani, Ashish / 2017 / Attention is all you need / Advances in neural information processing systems 30

[confproc] Abigail See / 2017 / Get to the point: Summarization with pointer generator networks / Proceedings of the 55th Annual Meeting of the Association for Computational, Linguistics

[web] / https://github.com/OpenNMT/OpenNMT-py / https://github.com/OpenNMT/OpenNMT-py

[book] Chin-Yew Lin / 2004 / Text Summarization Branches Out / Barcelona

This paper was written with support from the National Research Foundation of Korea.

KJCKorea
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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Text summarization of dialogue based on BERT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Text summarization of dialogue based on BERT

ABSTRACT

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* References for papers published after 2023 are currently being built.