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.
Abstract (translated)
评论是在线购物中客户进行购买决策时的重要资源。然而，面对大量的商品评价，顾客很难一一阅读并手动总结出突出的观点，这就催生了自动观点摘要系统的需要。先前的研究方法，无论是提取式的还是生成式的，都在自动生成基于方面的有根据的摘要方面面临挑战。在本文中，我们提出了一种新的摘要系统，该系统不仅从面向方面的角度捕捉到主要观点，并提供支持证据，而且还能适应不同的领域而不依赖于预定义的一组方面。我们的框架ASESUM通过提取以方面为中心的论点并衡量其重要性和有效性来总结与产品关键方面相关的观点。 我们在一个真实世界的数据集上进行了实验，结果表明，相较于现有和新的方法，我们提出的方法在捕捉原始评论中的多样视角方面具有优越性。
URL
https://arxiv.org/abs/2506.09917
