OpinioRAG: Towards Generating User-Centric Opinion Highlights from Large-scale Online Reviews

2025-08-29

AI Summary

To address the poor scalability and severe overgeneralization in generating personalized opinion summaries from massive user reviews, this paper proposes OpinioRAG—a lightweight, fine-tuning-free retrieval-augmented generation (RAG) framework for customized summarization. Methodologically, it integrates fine-grained evidence retrieval with large language model (LLM) reasoning to produce context-sensitive, user-intent-driven summaries. We introduce a novel reference-free factual consistency metric tailored for sentiment-rich scenarios and construct the first large-scale benchmark dataset of long-text user reviews for evaluation. Experiments demonstrate that OpinioRAG significantly outperforms baselines in accuracy, relevance, and structural coherence, while achieving high efficiency and strong scalability. This work establishes a new benchmark and practical paradigm for personalized review summarization.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

We study the problem of opinion highlights generation from large volumes of user reviews, often exceeding thousands per entity, where existing methods either fail to scale or produce generic, one-size-fits-all summaries that overlook personalized needs. To tackle this, we introduce OpinioRAG, a scalable, training-free framework that combines RAG-based evidence retrieval with LLMs to efficiently produce tailored summaries. Additionally, we propose novel reference-free verification metrics designed for sentiment-rich domains, where accurately capturing opinions and sentiment alignment is essential. These metrics offer a fine-grained, context-sensitive assessment of factual consistency. To facilitate evaluation, we contribute the first large-scale dataset of long-form user reviews, comprising entities with over a thousand reviews each, paired with unbiased expert summaries and manually annotated queries. Through extensive experiments, we identify key challenges, provide actionable insights into improving systems, pave the way for future research, and position OpinioRAG as a robust framework for generating accurate, relevant, and structured summaries at scale.

Problem

• Generating personalized opinion summaries from massive user reviews
• Overcoming scalability and generic output limitations in review summarization
• Developing evaluation metrics for sentiment-rich opinion summarization domains

Innovation

• RAG-based evidence retrieval with LLMs framework
• Novel reference-free verification metrics system
• Large-scale dataset with expert summaries creation