@inproceedings{saad-sanner-2025-q,
title = "{Q}-{STRUM} Debate: Query-Driven Contrastive Summarization for Recommendation Comparison",
author = "Saad, George-Kirollos and
Sanner, Scott",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.1170/",
doi = "10.18653/v1/2025.findings-acl.1170",
pages = "22765--22782",
ISBN = "979-8-89176-256-5",
abstract = "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 language-based item descriptions to clarify contrasts between them. However, existing state-of-the-art 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."
}
Markdown (Informal)
[Q-STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparison](https://preview.aclanthology.org/mtsummit-25-ingestion/2025.findings-acl.1170/) (Saad & Sanner, Findings 2025)
ACL