QQSUM：面向基于评论的产品问答的定量查询聚焦摘要新任务与模型

摘要

基于评论的产品问答系统（PQA）使电商平台能够通过挖掘用户评论中的洞见，自动解答顾客疑问。然而，现有的PQA系统仅能生成单一视角的答案，未能充分反映顾客意见的多样性。本文提出了一项新任务——定量查询聚焦摘要（QQSUM），旨在将多样化的顾客观点提炼为代表性关键点（KPs），并量化其普遍性，从而有效回应用户查询。尽管检索增强生成（RAG）在PQA中展现出潜力，但其生成的答案仍难以全面捕捉观点的多样性。为应对这一挑战，我们的模型QQSUM-RAG在RAG基础上进行了扩展，采用少样本学习联合训练一个面向KP的检索器和一个KP摘要生成器，实现了基于KP的摘要，能够捕捉多样且具代表性的意见。实验结果表明，QQSUM-RAG在文本质量和意见量化准确性方面均优于当前最先进的RAG基线模型。我们的源代码已公开于：https://github.com/antangrocket1312/QQSUMM。

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

QQSUM：面向基于评论的产品问答的定量查询聚焦摘要新任务与模型

QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering

摘要

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