Summary of An Empirical Analysis Of Diversity in Argument Summarization, by Michiel Van Der Meer et al.
An Empirical Analysis of Diversity in Argument Summarization
by Michiel van der Meer, Piek Vossen, Catholijn M. Jonker, Pradeep K. Murukannaiah
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper tackles the challenge of presenting high-level arguments in online discussions, focusing on capturing diversity to accommodate multiple perspectives. The authors introduce three aspects of diversity: opinions, annotators, and sources. They evaluate various approaches to Key Point Analysis (KPA), a popular argument summarization task, which highlights the struggles with representing minority opinions, handling data from diverse sources, and aligning with subjective annotations. The results show that both general-purpose large language models (LLMs) and dedicated KPA models exhibit these limitations, but have complementary strengths. Furthermore, diversifying training data may improve model generalizability. To address diversity in argument summarization, a combination of strategies is required to deal with subjectivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can present arguments online that make sure everyone’s voice is heard. The authors talk about something called “diversity” which means they want to show different opinions and perspectives. They test some ways to do this, like Key Point Analysis, but find that these methods have problems when it comes to sharing minority views, dealing with data from many sources, or agreeing with what people are saying. The results show that different models have their own strengths and weaknesses, but using more diverse training data might help them understand things better. Overall, the authors think we need a mix of strategies to make sure everyone’s voice is heard online. |
Keywords
» Artificial intelligence » Summarization
