QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering
- URL: http://arxiv.org/abs/2506.04020v1
- Date: Wed, 04 Jun 2025 14:50:32 GMT
- Title: QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering
- Authors: An Quang Tang, Xiuzhen Zhang, Minh Ngoc Dinh, Zhuang Li,
- Abstract summary: Review-based Product Question Answering (PQA) allows e-commerce platforms to automatically address customer queries.<n>Existing PQA systems generate answers with only a single perspective, failing to capture the diversity of customer opinions.<n>We introduce a novel task Quantitative Query-Focused Summarization (QQSUM), which aims to summarize diverse customer opinions into representative Key Points (KPs)
- Score: 17.61786406201472
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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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