Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

Milad Alshomary, Timon Gurcke, Shahbaz Syed, Philipp Heinisch, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, Henning Wachsmuth


Abstract
Key point analysis is the task of extracting a set of concise and high-level statements from a given collection of arguments, representing the gist of these arguments. This paper presents our proposed approach to the Key Point Analysis Shared Task, colocated with the 8th Workshop on Argument Mining. The approach integrates two complementary components. One component employs contrastive learning via a siamese neural network for matching arguments to key points; the other is a graph-based extractive summarization model for generating key points. In both automatic and manual evaluation, our approach was ranked best among all submissions to the shared task.
Anthology ID:
2021.argmining-1.19
Volume:
Proceedings of the 8th Workshop on Argument Mining
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venue:
ArgMining
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
184–189
Language:
URL:
https://aclanthology.org/2021.argmining-1.19
DOI:
10.18653/v1/2021.argmining-1.19
Bibkey:
Cite (ACL):
Milad Alshomary, Timon Gurcke, Shahbaz Syed, Philipp Heinisch, Maximilian Spliethöver, Philipp Cimiano, Martin Potthast, and Henning Wachsmuth. 2021. Key Point Analysis via Contrastive Learning and Extractive Argument Summarization. In Proceedings of the 8th Workshop on Argument Mining, pages 184–189, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Key Point Analysis via Contrastive Learning and Extractive Argument Summarization (Alshomary et al., ArgMining 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.argmining-1.19.pdf
Code
 webis-de/argmining-21