Generating Comparative Table by LLM-Based Product Review Summarization

Kanako Nakai, Takehiro Yamamoto, Hiroaki Ohshima

Abstract: In this study, we propose a method for generating a comparative table by summarizing product reviews using a Large Language Model (LLM). A comparative table summarizes, for each aspect of two products, the evaluations present in reviews and the number of reviews for each evaluation. By using an LLM to create the table, it becomes possible to generate the table on demand and to change the aspects to be compared depending on the products. The proposed method employs an LLM to extract aspects and their associated evaluations from reviews. These evaluations are summarized for each aspect to create a table mapping evaluations to their respective aspects. We used a review dataset from Rakuten Ichiba and automatically summarized reviews using an LLM to generate comparative tables. We conducted a user study to verify whether the tables are useful for comparing products. Based on the results of the user study, the proposed method was found to be more helpful for comparison than the baseline.

External IDs:dblp:conf/iiwas/NakaiYO25