Wang, GuanLi, WeihuaLai, EdmundBai, QuanWu, SSu, XXu, XKang, BH2026-05-142026-05-142024-11-15In: Wu, S., Su, X., Xu, X., Kang, B.H. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2024. Lecture Notes in Computer Science(), vol 15372. Springer. pp 29–4197898196002670302-97431611-3349http://hdl.handle.net/10292/21069The increase in online information has overwhelmed users with opinions and comments on various products and services, making decision-making a daunting task. Text summarization can help by distilling long or multiple documents into concise, relevant content. Recent advances in Large Language Models (LLM) have shown great potential in this area. The existing text summarization approaches often lack the “adaptive” nature required to capture diverse aspects in opinion summarization, which is particularly detrimental to users with specific preferences. In this paper, we introduce an Aspect-adaptive Knowledge-based Opinion Summarization model for product reviews. This model generates summaries that highlight specific aspects of reviews, providing users with targeted, relevant information quickly. Our extensive experiments with real-world datasets explicitly demonstrate that our model surpasses current state-of-the-art methods. It effectively adapts to user needs, producing efficient, aspect-focused summaries that help users make informed decisions based on their unique preferences.This is the Preprint of a conference paper presented at PKAW 2024: 20th Principle and Practice of Data and Knowledge Acquisition Workshop © 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. The publisher's version is available at doi: 10.1007/978-981-96-0026-7_346 Information and Computing Sciences4602 Artificial Intelligence4605 Data Management and Data Science4608 Human-Centred Computing4609 Information SystemsArtificial Intelligence & Image Processing46 Information and computing sciencesConference ContributionOpenAccess10.1007/978-981-96-0026-7_3