Aspect-Based Opinion Summarization with Argumentation Schemes

TL;DR

This paper introduces ASESUM, a novel aspect-based opinion summarization system that extracts and evaluates aspect-centric arguments to generate grounded, domain-adaptive summaries from online reviews.

Contribution

The paper presents a new summarization framework that captures diverse opinions with supporting evidence without relying on predefined aspects, improving over existing methods.

Findings

ASESUM outperforms existing methods in capturing diverse perspectives.

The system effectively adapts to different domains without predefined aspects.

Experimental results demonstrate the superiority of ASESUM in generating grounded summaries.

Abstract

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.