让商品问答更全面：用数据量化用户观点多样性
QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering
- 提出新任务QQSUM，从评论中提取观点并统计出现频率
- 模型在文本质量和观点量化上均优于现有方法
- 适合电商客服、产品分析等需洞察用户意见的场景
基于评论的商品问答（PQA）允许电商平台通过用户评论自动回答客户问题。然而，现有PQA系统仅生成单一视角的答案，难以反映用户意见的多样性。本文提出新任务定量聚焦式摘要（QQSUM），旨在将多样化的用户观点归纳为代表性关键点（KPs），并量化其出现频率以有效回应用户查询。尽管检索增强生成（RAG）在PQA中展现潜力，其生成答案仍未能充分捕捉观点多样性。为此，我们提出扩展版RAG模型QQSUM-RAG，采用少样本学习联合训练面向关键点的检索器与摘要生成器，实现基于关键点的摘要，能有效捕捉多样且具代表性的用户意见。实验表明，相较于先进RAG基线，QQSUM-RAG在文本质量与观点量化准确性上均有显著提升。源代码已开源。
原文摘要 · Abstract (English)
Review-based Product Question Answering (PQA) allows e-commerce platforms to automatically address customer queries by leveraging insights from user reviews. However, existing PQA systems generate answers with only a single perspective, failing to capture the diversity of customer opinions. In this paper we introduce a novel task Quantitative Query-Focused Summarization (QQSUM), which aims to summarize diverse customer opinions into representative Key Points (KPs) and quantify their prevalence to effectively answer user queries. While Retrieval-Augmented Generation (RAG) shows promise for PQA, its generated answers still fall short of capturing the full diversity of viewpoints. To tackle this challenge, our model QQSUM-RAG, which extends RAG, employs few-shot learning to jointly train a KP-oriented retriever and a KP summary generator, enabling KP-based summaries that capture diverse and representative opinions. Experimental results demonstrate that QQSUM-RAG achieves superior performance compared to state-of-the-art RAG baselines in both textual quality and quantification accuracy of opinions. Our source code is available at: https://github.com/antangrocket1312/QQSUMM
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