From Arguments to Key Points: Towards Automatic Argument Summarization

Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, Noam Slonim


Abstract
Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed key points, each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduce a novel large-scale dataset for this task. We report empirical results for an extensive set of experiments with this dataset, showing promising performance.
Anthology ID:
2020.acl-main.371
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4029–4039
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.371
DOI:
10.18653/v1/2020.acl-main.371
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.371.pdf
Video:
 http://slideslive.com/38929133