ARQUSUMM: Argument-aware Quantitative Summarization of Online Conversations

2025-11-21

AI Summary

Existing dialogue summarization methods overlook argumentative structure, failing to explicitly model claim–reason relationships or quantify argument strength. To address this, we propose the novel task of *argument-aware quantitative summarization*, which integrates argumentation theory with few-shot learning from large language models to enable fine-grained identification of claim–reason dependencies. We further design a structure-aware clustering algorithm that automatically aggregates arguments and quantifies their collective support strength. Unlike conventional approaches, our framework preserves summary readability while significantly enhancing explicitness of argument structure and accuracy of strength assessment. Experimental results demonstrate that our method outperforms state-of-the-art baselines across summary quality, argument coverage, and quantitative precision—thereby enabling more nuanced understanding and representation of diverse perspectives on contentious topics.

Technology Category

• Natural Language Processing: Summarization
• Knowledge Representation and Reasoning: Argumentation
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Summarizing argument structures in online conversations
• Quantifying argument strength with claim-reason relationships
• Overcoming limitations of existing conversation summarization methods

Innovation

• LLM few-shot learning identifies claim-reason relationships
• Argument structure-aware clustering aggregates and quantifies arguments
• Generates summaries with argument strength measurements