Aspect-based Key Point Analysis for Quantitative Summarization of Reviews (2024)

Abstract: 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

## introduction summarization of user reviews on the online marketplace has become essential both for businesses to improve their product and service qualities and for customers to make purchasing decisions. although the star ratings aggregated from customer reviews are widely used to measure quality of service for business entities @xcite , they can not explain specific details to achieve business inteligence and informed decision. early studies on review summarization focus on textual summaries that only represent the major opinions in reviews @xcite but ignore the minority opinions and fail to quantify the opinion prevalence. recently, the quantitative view was introduced to review summarization under the novel framework named key point analysis (kpa) @xcite . kpa studies were initially extractive and developed for argument summarization @xcite , and are then adapted for business reviews @xcite . kpa consists of two subtasks, namely key point extraction, which extracts salient sentences as kps, and key point matching, which quantifies the prevalence of kps as the number of matching comments in reviews foot_0 . more recent kpa studies used abstractive summarization models to generate salient kps @xcite . whether extractive or abstractive approaches, existing kpa studies still perform kp extraction and matching at the sentence level, which has two major issues. first, the extracted kps (i.e., short sentences) can contain overlapping opinions on the same aspects, causing high kp redundancy. subsequently, with both comments and kps containing multiple opinions, sentence-based matching of kps to comment then becomes ineffective and results in inaccrurate kp prevalence. to address the two above issues, we propose aspect-based key point analysis (abkpa), a novel and more effective extractive kpa framework for review summarization. abkpa comprises two key components: aspect-based kp extraction and aspect-based kp matching. first, leveraging the fine-grained aspect-based sentiment analysis (absa) model @xcite for review comments, abkpa extracts kps free from redundancy and containing single opinions. next, again making use of readily available absa re- our main contributions are: (1) we propose aspect-based key point analysis (abkpa), a novel summarization framework for business reviews. abkpa addresses the kpa shortcomings in sentence-based kp extraction and matching, which extract kps with overlapping opinions and falsely matches kps to long review comments containing multiple opinions. (2) core to abkpa is the use of fine-grained absa model to extract aspect-focused kps without redundancy. (3) importantly, using fine-grained absa tagging to automatically generate and annotate silver labels for aspect-sentiment matching examples, we employed contrastive learning and devised an aspect-based kp matching model for more accurate kp quantification on business reviews. ## related work based on the form of summaries, review summarization studies can be broadly grouped into three classes: aspect-based structured summarization, textual summarization, and key point analysis. ## aspect-based key point analysis we propose the abkpa framework, with the training and inference phases presented in figure 1 . abkpa mainly leverages absa resources during its inference phase to enhance the quality of kps through aspect-based kp extraction (section 3.1), and precisely map comments with multiple opinions to various kps via aspect-based kp matching (section 3.2). notably, in the training phase, abkpa again utilizes absa for automatic construction and labelling of aspect-sentiment matching pairs without human annotation (section 3.3), which can effectively bootstrap our aspect-based kp matching model through contrastive learning. ## conclusions this paper proposed aspect-based key point analysis (abkpa), a framework that effectively makes use of absa resources in business reviews to enhance multiple tasks of kpa. abkpa addresses the major shortcoming of previous sentence-based kpa studies on the insufficient capture of comment's opinion and generation of redundant kps. first, we leverage fine-grained absa to extract kps by their aspects from comments, which significantly eliminates overlapping kps compared to previous kpa studies. secondly, leveraging absa for contrastive learning, we develop an effective aspectbased kp matching model for mapping various kps to comments with multiple opinions, which results in more accurate opinion quantification.