Summary of Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning, by Xiao Li et al.
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning
by Xiao Li, Yong Jiang, Shen Huang, Pengjun Xie, Gong Cheng, Fei Huang
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper addresses the challenge of Key Point Analysis (KPA) in argument mining by proposing a novel approach for summarizing multiple arguments into concise key points. Existing methods rely on semantic similarity, but this approach ignores inter-cluster relationships between arguments that don’t share key points. Our proposed model uses pairwise generation and graph partitioning to simultaneously identify shared key points and generate scores indicating their presence. We train a generative model to score the existence of shared key points among pairs of arguments and then construct an argument graph with edge weights based on these scores. A graph partitioning algorithm is used to group arguments sharing the same key points into subgraphs. Experimental results show our proposed model outperforms previous models on both ArgKP and QAM datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a problem called Key Point Analysis (KPA). KPA helps us summarize many ideas or arguments into just a few important points. Right now, there are some ways to do this, but they don’t work very well because they only look at similar ideas and ignore other connections between them. The authors of this paper have a new idea for doing KPA that uses two parts: one to find common points between ideas and another to group those ideas together based on what they have in common. They tested their approach on some datasets and found it works better than the old ways. |
Keywords
» Artificial intelligence » Generative model
