Papers by Minh Dinh

2 papers
Prompted Aspect Key Point Analysis for Quantitative Review Summarization (2024.acl-long)

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Challenge: Recent abstractive approaches generate KPs based on sentences, resulting in overlapping and hallucinated opinions.
Approach: They propose to use supervised learning to extract short sentences as key points before matching them to review comments for quantification of KP prevalence.
Outcome: The proposed framework achieves state-of-the-art performance on Yelp and SPACE.
Aspect-based Key Point Analysis for Quantitative Summarization of Reviews (2024.findings-eacl)

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Challenge: Existing studies on review summarization use only major opinions, but ignore minority opinions and fail to quantify opinion prevalence.
Approach: They propose a framework for quantitative review summarization using aspect-based key point analysis (ABKPA) they use aspect-basic sentiment analysis to automatically annotate silver labels for matching aspect-sentiment pairs .
Outcome: The proposed framework outperforms state-of-the-art baselines on Yelp reviews on five business categories.

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