QQSUM：一種針對基於評論的產品問答之定量查詢聚焦摘要的新任務與模型

摘要

基於評論的產品問答（PQA）使電子商務平台能夠利用用戶評論的洞察來自動回應顧客查詢。然而，現有的PQA系統僅生成單一視角的答案，未能捕捉到顧客意見的多樣性。本文中，我們引入了一項新任務——定量查詢聚焦摘要（QQSUM），旨在將多樣的顧客意見總結成具有代表性的關鍵點（KPs），並量化其普遍性，以有效回答用戶查詢。儘管檢索增強生成（RAG）在PQA中顯示出潛力，但其生成的答案仍未能全面捕捉觀點的多樣性。為應對這一挑戰，我們的模型QQSUM-RAG，作為RAG的擴展，採用少樣本學習聯合訓練一個面向KP的檢索器和一個KP摘要生成器，從而實現基於KP的摘要，捕捉多樣且具代表性的意見。實驗結果表明，QQSUM-RAG在文本質量和意見量化準確性方面均優於最先進的RAG基線模型。我們的源代碼可在以下網址獲取：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：一種針對基於評論的產品問答之定量查詢聚焦摘要的新任務與模型

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

摘要

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