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

翻译：关键点分析旨在提供量化摘要，将关键点作为简洁的文本摘要并量化其普遍性。已有文献报道了针对论点与评论的关键点分析研究。大多数针对评论的关键点分析研究采用监督学习，先提取短句作为关键点，再将关键点与评论内容进行匹配以量化其普遍性。最近的生成式方法虽能生成关键点，但仍基于句子，常导致关键点存在观点重叠、虚构意见及量化不准确的问题。本文提出基于提示的方面关键点分析用于量化评论摘要。该方法利用方面情感分析及基于大型语言模型的提示上下文学习，为商业实体生成并量化基于方面的关键点，从而获得忠实的关键点与准确的量化结果，并无需大量标注数据进行监督训练。在流行的评论数据集Yelp及面向方面的评论摘要数据集SPACE上的实验表明，本框架达到了最先进的性能。源代码与数据可在以下网址获取：https://github.com/antangrocket1312/PAKPA