Online conversations have become more prevalent on public discussion platforms (e.g. Reddit). With growing controversial topics, it is desirable to summarize not only diverse arguments, but also their rationale and justification. Early studies on text summarization focus on capturing general salient information in source documents, overlooking the argumentative nature of online conversations. Recent research on conversation summarization although considers the argumentative relationship among sentences, fail to explicate deeper argument structure within sentences for summarization. In this paper, we propose a novel task of argument-aware quantitative summarization to reveal the claim-reason structure of arguments in conversations, with quantities measuring argument strength. We further propose ARQUSUMM, a novel framework to address the task. To reveal the underlying argument structure within sentences, ARQUSUMM leverages LLM few-shot learning grounded in the argumentation theory to identify propositions within sentences and their claim-reason relationships. For quantitative summarization, ARQUSUMM employs argument structure-aware clustering algorithms to aggregate arguments and quantify their support. Experiments show that ARQUSUMM outperforms existing conversation and quantitative summarization models and generate summaries representing argument structures that are more helpful to users, of high textual quality and quantification accuracy.

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/

Key Point Analysis (KPA) aims for quantitative summarization that provides key points (KPs) as succinct textual summaries and quantities measuring their prevalence.KPA studies for arguments and reviews have been reported in the literature.A majority of KPA studies for reviews adopt supervised learning to extract short sentences as KPs before matching KPs to review comments for quantification of KP prevalence.Recent abstractive approaches still generate KPs based on sentences, often leading to KPs with overlapping and hallucinated opinions, and inaccurate quantification.In this paper, we propose Prompted Aspect Key Point Analysis (PAKPA) for quantitative review summarization.PAKPA employs aspect sentiment analysis and prompted in-context learning with Large Language Models (LLMs) to generate and quantify KPs grounded in aspects for business entities, which achieves faithful KPs with accurate quantification, and removes the need for large amounts of annotated data for supervised training.Experiments on the popular review dataset Yelp and the aspect-oriented review summarization dataset SPACE show that our framework achieves state-of-the-art performance.Source code and data are available at: https://github.com/ antangrocket1312/PAKPA Key Point Prevalence Matching Comments Friendly and helpful staff.46 From the minute I walked in the door the staff has treated me with such kindness and respect, there are not enough words I can say of how much gratitude I have for the staff here.It's a fine hotel, pretty basic, and all of the staff members we encountered were quite friendly.Excellent location for business travelers.37 Its a great location for business travelers since I can stay here and always be going the opposite way of traffic.*This hotel has an IDEAL location, the shuttle was perfect, and we got a great deal on priceline.Clean and comfortable rooms.32 *You will enjoy the breathtaking views from your spacious, clean and comfortable room.The rooms were nice and clean and the bed was comfortable.Convenient and helpful shuttle service.12 *This hotel has an IDEAL location, the shuttle was perfect, and we got a great deal on priceline.I would also like to give a shout out to the terrific shuttle drivers Jeff and Rod who were so great to my family group.Amazing view of Vanderbilt football stadium or The Parthenon.10 *You will enjoy the breathtaking views from your spacious, clean and comfortable room.Then the surprise came when we opened the curtains to see a full on view of the Vanderbilt football stadium.