Aspect-Based Opinion Summarization with Argumentation Schemes
This work addresses the need for automated, grounded aspect-centric opinion summarization to help customers make purchase decisions, representing a novel method for a known bottleneck in the field.
The paper tackles the problem of generating aspect-based opinion summaries from online reviews by proposing ASESUM, a system that extracts aspect-centric arguments and measures their salience and validity, achieving superior performance in capturing diverse perspectives compared to existing methods.
Reviews are valuable resources for customers making purchase decisions in online shopping. However, it is impractical for customers to go over the vast number of reviews and manually conclude the prominent opinions, which prompts the need for automated opinion summarization systems. Previous approaches, either extractive or abstractive, face challenges in automatically producing grounded aspect-centric summaries. In this paper, we propose a novel summarization system that not only captures predominant opinions from an aspect perspective with supporting evidence, but also adapts to varying domains without relying on a pre-defined set of aspects. Our proposed framework, ASESUM, summarizes viewpoints relevant to the critical aspects of a product by extracting aspect-centric arguments and measuring their salience and validity. We conduct experiments on a real-world dataset to demonstrate the superiority of our approach in capturing diverse perspectives of the original reviews compared to new and existing methods.