Matching The Statements: A Simple and Accurate Model for Key Point Analysis

Hoang Phan, Long Nguyen, Long Nguyen, Khanh Doan


Abstract
Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic. Furthermore, KPA allows quantifying the coverage of each summary by counting its matched arguments. With the aim of creating high-quality summaries, it is necessary to have an in-depth understanding of each individual argument as well as its universal semantic in a specified context. In this paper, we introduce a promising model, named Matching the Statements (MTS) that incorporates the discussed topic information into arguments/key points comprehension to fully understand their meanings, thus accurately performing ranking and retrieving best-match key points for an input argument. Our approach has achieved the 4th place in Track 1 of the Quantitative Summarization – Key Point Analysis Shared Task by IBM, yielding a competitive performance of 0.8956 (3rd) and 0.9632 (7th) strict and relaxed mean Average Precision, respectively.
Anthology ID:
2021.argmining-1.17
Volume:
Proceedings of the 8th Workshop on Argument Mining
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic
Venues:
ArgMining | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
165–174
Language:
URL:
https://aclanthology.org/2021.argmining-1.17
DOI:
10.18653/v1/2021.argmining-1.17
Bibkey:
Cite (ACL):
Hoang Phan, Long Nguyen, Long Nguyen, and Khanh Doan. 2021. Matching The Statements: A Simple and Accurate Model for Key Point Analysis. In Proceedings of the 8th Workshop on Argument Mining, pages 165–174, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Matching The Statements: A Simple and Accurate Model for Key Point Analysis (Phan et al., ArgMining 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.argmining-1.17.pdf
Code
 viethoang1512/kpa
Data
ArgKP-2021