Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparison

Query-driven recommendation with unknown items poses a challenge for users to understand why certain items are appropriate for their needs.Query-driven Contrastive Summarization (QCS) is a methodology designed to address this issue by leveraging languagebased item descriptions to clarify contrasts between them.However, existing state-of-theart contrastive summarization methods such as STRUM-LLM fall short of this goal.To overcome these limitations, we introduce Q-STRUM Debate, a novel extension of STRUM-LLM that employs debate-style prompting to generate focused and contrastive summarizations of item aspects relevant to a query.Leveraging modern large language models (LLMs) as powerful tools for generating debates, Q-STRUM Debate provides enhanced contrastive summaries.Experiments across three datasets demonstrate that Q-STRUM Debate yields significant performance improvements over existing methods on key contrastive summarization criteria, thus introducing a novel and performant debate prompting methodology for QCS.