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Argument Extraction for Key Point Generation Using MMR-Based Methods

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Title: Argument Extraction for Key Point Generation Using MMR-Based Methods
Authors: Shirafuji, Daiki Browse this author
Rzepka, Rafal Browse this author →KAKEN DB
Araki, Kenji Browse this author →KAKEN DB
Keywords: Task analysis
Bit error rate
Computational modeling
Argument aggregation
argument mining
key points
machine learning
natural language processing
text summarization
Issue Date: 27-Jul-2021
Publisher: IEEE (Institute of Electrical and Electronics Engineers)
Journal Title: IEEE Access
Volume: 9
Start Page: 103091
End Page: 103109
Publisher DOI: 10.1109/ACCESS.2021.3097976
Abstract: When people debate, they want to familiarize themselves with a whole range of arguments about a given topic in order to deepen their knowledge and inspire new claims. However, the amount of differently phrased arguments is humongous, making the process of processing them time-consuming. In spite of many works on using arguments (e.g. counter-argument generation), there is only a few studies on argument aggregation. To address this problem, we propose a new task in argument mining - Argument Extraction, which gathers similar arguments into key points, usually single sentences describing a set of arguments for a given debate topic. Such a short summary of related arguments has been manually created in previous research, while in our research key point generation becomes fully automatic, saving time and cost. As the first step of key point generation we explore existing similarity calculation methods, i.e. Sentence-BERT and MoverScore to investigate their performance. Next, we propose a combination of argument similarity and Maximal Marginal Relevance (MMR) for extracting key phrases to be utilized in our novel task of Argument Extraction. Experimental results show that MoverScore-based MMR outperforms strong baselines covering 72.5% of arguments when eleven or more arguments are extracted. This percentage is almost identical with the cover rate of human-made key points.
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Type: article
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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