Key Point Analysis (KPA) is originally for summarizing arguments, where short sentences containing salient viewpoints are extracted as key points (KPs) and quantified for their prevalence as salience scores. Recently, KPA was applied to summarize reviews, but the study still relies on sentence-based KP extraction and matching, which leads to two issues: sentence-based extraction can result in KPs of overlapping opinions on the same aspects, and sentence-based matching of KP to review comment can be inaccurate, resulting in inaccurate salience scores. To address the above issues, in this paper, we propose Aspect-based Key Point Analysis (ABKPA), a novel framework for quantitative review summarization. Leveraging the readily available aspect-based sentiment analysis (ABSA) resources of reviews to automatically annotate silver labels for matching aspect-sentiment pairs, we propose a contrastive learning model to effectively match KPs to reviews and quantify KPs at the aspect level. Especially, the framework ensures extracting KP of distinct aspects and opinions, leading to more accurate opinion quantification. Experiments on five business categories of the popular Yelp review dataset show that ABKPA outperforms state-of-the-art baselines. Source code and data are available at: https://github.com/antangrocket1312/ABKPA

Key Point Analysis (KPA) aims for quantitative summarization that provides key points (KPs) as succinct textual summaries and quantities measuring their prevalence. KPA studies for arguments and reviews have been reported in the literature. A majority of KPA studies for reviews adopt supervised learning to extract short sentences as KPs before matching KPs to review comments for quantification of KP prevalence. Recent abstractive approaches still generate KPs based on sentences, often leading to KPs with overlapping and hallucinated opinions, and inaccurate quantification. In this paper, we propose Prompted Aspect Key Point Analysis (PAKPA) for quantitative review summarization. PAKPA employs aspect sentiment analysis and prompted in-context learning with Large Language Models (LLMs) to generate and quantify KPs grounded in aspects for business entities, which achieves faithful KPs with accurate quantification, and removes the need for large amounts of annotated data for supervised training. Experiments on the popular review dataset Yelp and the aspect-oriented review summarization dataset SPACE show that our framework achieves state-of-the-art performance. Source code and data are available at: https://github.com/antangrocket1312/PAKPA