[2603.19277] MOSAIC: Modular Opinion Summarization using Aspect Identification and Clustering

Computer Science > Computation and Language arXiv:2603.19277 (cs) [Submitted on 1 Mar 2026] Title:MOSAIC: Modular Opinion Summarization using Aspect Identification and Clustering Authors:Piyush Kumar Singh, Jayesh Choudhari View a PDF of the paper titled MOSAIC: Modular Opinion Summarization using Aspect Identification and Clustering, by Piyush Kumar Singh and 1 other authors View PDF HTML (experimental) Abstract:Reviews are central to how travelers evaluate products on online marketplaces, yet existing summarization research often emphasizes end-to-end quality while overlooking benchmark reliability and the practical utility of granular insights. To address this, we propose MOSAIC, a scalable, modular framework designed for industrial deployment that decomposes summarization into interpretable components, including theme discovery, structured opinion extraction, and grounded summary generation. We validate the practical impact of our approach through online A/B tests on live product pages, showing that surfacing intermediate outputs improves customer experience and delivers measurable value even prior to full summarization deployment. We further conduct extensive offline experiments to demonstrate that MOSAIC achieves superior aspect coverage and faithfulness compared to strong baselines for summarization. Crucially, we introduce opinion clustering as a system-level component and show that it significantly enhances faithfulness, particularly under the noisy and redundant ...