Aspect-Aware Decomposition for Opinion Summarization

ACL ARR 2025 February Submission4063 Authors

Abstract:

Opinion summarization plays a key role in deriving meaningful insights from large-scale online reviews. To make this process more explainable and grounded, we propose a modular approach guided by review aspects (e.g., cleanliness for hotel reviews) which separates the tasks of aspect identification, opinion consolidation, and meta-review synthesis, enabling greater transparency and ease of inspection.
We conduct extensive experiments across datasets representing scientific research, business, and product domains. Results show that our method generates more grounded summaries compared to strong baseline models, as verified through automated and human evaluations. Additionally, our modular approach, which incorporates reasoning based on review aspects, produces more informative intermediate outputs than knowledge-agnostic decomposed prompting. These intermediate outputs can also effectively support humans in summarizing opinions from large volumes of reviews.

Paper Type: Long

Research Area: Summarization

Research Area Keywords: abstractive summarisation, multi-document summarization, long-form summarization

Contribution Types: NLP engineering experiment, Theory

Languages Studied: English

Submission Number: 4063