Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning

Xiao Li, Yong Jiang, Shen Huang, Pengjun Xie, Gong Cheng, Fei Huang


Abstract
Key Point Analysis (KPA), the summarization of multiple arguments into a concise collection of key points, continues to be a significant and unresolved issue within the field of argument mining. Existing models adapt a two-stage pipeline of clustering arguments or generating key points for argument clusters. This approach rely on semantic similarity instead of measuring the existence of shared key points among arguments. Additionally, it only models the intra-cluster relationship among arguments, disregarding the inter-cluster relationship between arguments that do not share key points. To address these limitations, we propose a novel approach for KPA with pairwise generation and graph partitioning. Our objective is to train a generative model that can simultaneously provide a score indicating the presence of shared key point between a pair of arguments and generate the shared key point. Subsequently, to map generated redundant key points to a concise set of key points, we proceed to construct an arguments graph by considering the arguments as vertices, the generated key points as edges, and the scores as edge weights. We then propose a graph partitioning algorithm to partition all arguments sharing the same key points to the same subgraph. Notably, our experimental findings demonstrate that our proposed model surpasses previous models when evaluated on both the ArgKP and QAM datasets.
Anthology ID:
2024.naacl-long.315
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5657–5667
Language:
URL:
https://aclanthology.org/2024.naacl-long.315
DOI:
Bibkey:
Cite (ACL):
Xiao Li, Yong Jiang, Shen Huang, Pengjun Xie, Gong Cheng, and Fei Huang. 2024. Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5657–5667, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (Li et al., NAACL 2024)
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https://aclanthology.org/2024.naacl-long.315.pdf
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