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
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4029–4039
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.371
- DOI:
- 10.18653/v1/2020.acl-main.371
- Cite (ACL):
- Roy Bar-Haim, Lilach Eden, Roni Friedman, Yoav Kantor, Dan Lahav, and Noam Slonim. 2020. From Arguments to Key Points: Towards Automatic Argument Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4029–4039, Online. Association for Computational Linguistics.
- Cite (Informal):
- From Arguments to Key Points: Towards Automatic Argument Summarization (Bar-Haim et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.acl-main.371.pdf
- Data
- IBM-Rank-30k, MultiNLI, SNLI