QQSUM: A Novel Task and Model of Quantitative Query-Focused
  Summarization for Review-based Product Question Answering
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
684962453614057188acbfb8
An Quang Tang
684962453614057188acbfb9
Xiuzhen Zhang
684962453614057188acbfba
Minh Ngoc Dinh
684962453614057188acbfbb
Zhuang Li
QQSUM: A Novel Task and Model of Quantitative Query-Focused
  Summarization for Review-based Product Question Answering
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-RAG extends RAG to generate diverse and representative summaries of customer opinions for product question answering by integrating few-shot learning.
Quantitative Query-Focused Summarization QQSUM Retrieval-Augmented Generation RAG few-shot learning Key Points KP-based summaries