Aspect-adaptive Knowledge-based Opinion Summarization
Date
Authors
Wang, Guan
Li, Weihua
Lai, Edmund
Bai, Quan
Supervisor
Item type
Conference Contribution
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Nature Singapore
Abstract
The 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.Description
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science, 4608 Human-Centred Computing, 4609 Information Systems, Artificial Intelligence & Image Processing, 46 Information and computing sciences
Source
In: 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 29–41
Publisher's version
Rights statement
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_3