"This Suits You the Best": Query Focused Comparative Explainable Summarization
Abstract: Product recommendations inherently involve comparisons, yet traditional opinion summarization often fails to provide holistic comparative insights. We propose the novel task of generating Query-Focused Comparative Explainable Summaries (QF-CES) using Multi-Source Opinion Summarization (M-OS). To address the lack of query-focused recommendation datasets, we introduce MS-Q2P, comprising 7,500 queries mapped to 22,500 recommended products with metadata. We leverage Large Language Models (LLMs) to generate tabular comparative summaries with query-specific explanations. Our approach is personalized, privacy-preserving, recommendation engine-agnostic, and category-agnostic. M-OS, as an intermediate step, reduces inference latency by approximately 40% compared to the direct input approach (DIA), which processes raw data directly. We evaluate open-source and proprietary LLMs for generating and assessing QF-CES. Extensive evaluations using QF-CES-PROMPT across five dimensions (clarity, faithfulness, informativeness, format adherence, and query relevance) showed an average Spearman correlation of 0.74 with human judgments, indicating its potential for QF-CES evaluation.
Paper Type: Long
Research Area: Summarization
Research Area Keywords: Query-focused Comparitive Summarization, Opinion Summarization, Explainable Recommendation
Contribution Types: Model analysis & interpretability, Publicly available software and/or pre-trained models, Data resources
Languages Studied: English
Submission Number: 470
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