MOSAIC: Modular Opinion Summarization using Aspect Identification and Clustering
arXiv:2603.19277v1 Announce Type: new 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 cluster
arXiv:2603.19277v1 Announce Type: new 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 conditions typical of user reviews. Finally, we identify reliability limitations in the standard SPACE dataset and release a new open-source tour experience dataset (TRECS) to enable more robust evaluation.
Executive Summary
This paper presents MOSAIC, a modular framework for opinion summarization that addresses existing limitations in benchmark reliability and practical utility. MOSAIC decomposes summarization into interpretable components, including theme discovery, structured opinion extraction, and grounded summary generation. The authors validate MOSAIC through online A/B tests and offline experiments, demonstrating its superiority in aspect coverage and faithfulness. The framework also introduces opinion clustering as a system-level component, which enhances faithfulness under noisy and redundant conditions. The study highlights reliability limitations in the standard SPACE dataset and releases a new open-source tour experience dataset (TRECS). Overall, MOSAIC offers a scalable and modular approach to opinion summarization that prioritizes practical utility and benchmark reliability.
Key Points
- ▸ MOSAIC is a modular framework for opinion summarization that addresses existing limitations in benchmark reliability and practical utility.
- ▸ MOSAIC decomposes summarization into interpretable components, including theme discovery, structured opinion extraction, and grounded summary generation.
- ▸ The authors validate MOSAIC through online A/B tests and offline experiments, demonstrating its superiority in aspect coverage and faithfulness.
Merits
Modular and Scalable Design
MOSAIC's modular design allows for scalability and flexibility in deployment, making it suitable for industrial use cases.
Improved Faithfulness and Aspect Coverage
MOSAIC achieves superior aspect coverage and faithfulness compared to strong baselines for summarization, demonstrating its effectiveness in opinion summarization.
Demerits
Dataset Limitations
The study highlights reliability limitations in the standard SPACE dataset, which may impact the generalizability of the results.
Limited Evaluation of MOSAIC in Real-World Scenarios
While MOSAIC is validated through online A/B tests, the study could benefit from further evaluation in real-world scenarios to demonstrate its practical impact.
Expert Commentary
MOSAIC presents a significant contribution to the field of opinion summarization by addressing existing limitations in benchmark reliability and practical utility. The framework's modular design and interpretable components make it suitable for industrial deployment, and its performance in online A/B tests and offline experiments is encouraging. However, further evaluation in real-world scenarios and consideration of the limitations in the standard SPACE dataset are necessary to fully assess the framework's potential. Additionally, the study's findings on the importance of opinion clustering as a system-level component are noteworthy, highlighting the need for further research in this area.
Recommendations
- ✓ Future research should focus on evaluating MOSAIC in real-world scenarios to demonstrate its practical impact and address the limitations in the standard SPACE dataset.
- ✓ Opinion clustering should be further researched as a system-level component in opinion summarization, particularly under noisy and redundant conditions.
Sources
Original: arXiv - cs.CL
