Debate divides: Argument relation-based contrastive opinion summarization via multi-task learning for online discussions

Contrastive opinion summarization(COS) aims to generate summaries that can see both sides of an argument. Existing studies mostly focus on summarizing popular opinions expressed in comments, often ignoring divergent opinions. While some studies use sentiment-based frameworks to compare opinions on the same topic, relying solely on sentiment polarity proves inadequate for distinguishing nuanced differences in opinion. Additionally, many methods extract topics directly from comments without considering their relevance to the associated reading content. In this paper, we propose a novel Topic-level Contrastive Opinion Summarization model for online discussions, called ARCOS, which leverages argument relations between comments to identify divergent opinion pairs. Inspired by cognitive map theory, ARCOS first extracts topics from a keyword co-occurrence graph generated from the reading content. It then uses a multi-task learning network to predict argument relations between comments and align them with the extracted content topics. Based on argument relations, ARCOS selects representative contrastive comment pairs from comments on the same topic, and employs a large language model to produce a divergent opinion summary. Experimental results on a newly created benchmark dataset, RNCOS, show that ARCOS outperforms baselines in various sub-tasks and generates high-quality summaries for divergent opinions about specific content topics.