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

An Quang Tang, Xiuzhen Zhang, Minh Ngoc Dinh, Zhuang Li


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
Anthology ID:
2025.acl-long.1015
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
20810–20831
Language:
URL:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1015/
DOI:
Bibkey:
Cite (ACL):
An Quang Tang, Xiuzhen Zhang, Minh Ngoc Dinh, and Zhuang Li. 2025. QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20810–20831, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
QQSUM: A Novel Task and Model of Quantitative Query-Focused Summarization for Review-based Product Question Answering (Tang et al., ACL 2025)
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PDF:
https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1015.pdf