用大模型生成商品评论摘要，让总结更精准可读。
End-to-End Aspect-Guided Review Summarization at Scale
- 先提取评论中的观点-情感对，再选高频观点构造提示词
- 大规模测试显示摘要更贴近真实用户反馈
- 适合做电商评论分析或用户洞察的研究者
我们提出一个基于大语言模型的可扩展系统，将基于观点的情感分析（ABSA）与引导式摘要结合，为Wayfair平台生成简洁且可解释的商品评论摘要。该方法首先从单个评论中提取并整合观点-情感对，筛选每个商品最频繁出现的观点，并据此采样代表性评论。这些评论用于构建结构化提示词，引导大模型生成基于真实客户反馈的摘要。我们通过大规模线上A/B测试验证了系统的实际效果。此外，本文介绍了实时部署策略，并发布了一个包含1180万条匿名客户评论的数据集，覆盖92000个产品，包含提取出的观点和生成的摘要，以支持未来在观点引导式评论摘要领域的研究。
原文摘要 · Abstract (English)
We present a scalable large language model (LLM)-based system that combines aspect-based sentiment analysis (ABSA) with guided summarization to generate concise and interpretable product review summaries for the Wayfair platform. Our approach first extracts and consolidates aspect-sentiment pairs from individual reviews, selects the most frequent aspects for each product, and samples representative reviews accordingly. These are used to construct structured prompts that guide the LLM to produce summaries grounded in actual customer feedback. We demonstrate the real-world effectiveness of our system through a large-scale online A/B test. Furthermore, we describe our real-time deployment strategy and release a dataset of 11.8 million anonymized customer reviews covering 92,000 products, including extracted aspects and generated summaries, to support future research in aspect-guided review summarization.
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