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
- Editors:
- Khalid Al-Khatib, Yufang Hou, Manfred Stede
- Venue:
- ArgMining
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.argmining-1.17.pdf
- Code
- viethoang1512/kpa
- Data
- ArgKP-2021