基于论据模式的评论摘要，自动提炼关键观点与证据。
Aspect-Based Opinion Summarization with Argumentation Schemes
- 用论据模式提取产品评价中的核心观点与支撑证据
- 无需预定义方面，可适应不同领域且覆盖多元视角
- 在真实数据集上优于现有方法，更全面捕捉原始评论观点
在线购物中，评论是用户决策的重要参考，但海量评论难以手动总结。现有抽取式或生成式方法难以自动生成以方面为中心、有依据的摘要。本文提出ASESUM框架，通过提取方面相关的论据并评估其显著性与有效性，自动生成包含支持证据的方面中心摘要。该方法不依赖预定义方面，可适应不同领域。在真实数据集上的实验表明，相比新旧方法，本方案能更全面地捕捉原始评论中的多样化观点。
原文摘要 · Abstract (English)
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.
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