Q - STRUM Debate: Query-Driven Contrastive Summarization for Recommendation Comparison (2025)

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.

## introduction in query-driven recommendation settings such as hotels, restaurants, or travel, where items may be a priori unknown to users, language-based item descriptions can help users make informed choices. however, understanding the trade-offs between choices becomes challenging given the abundance of information from objective sources, like wikipedia or travel guides, and opinion-rich subjective sources, such as tripadvisor and amazon reviews @xcite . fortunately, query-driven contrastive summarization (qcs) offers a principled solution to these challenges by providing succinct comparative summaries of items. however, many traditional qcs methods often rely on complex extraction, ranking, and diversification algorithms that may fail to find clear contrasts, leaving users to sift through extensive information @xcite . fortunately, the emergence of large language models (llms) has revolutionized qcs capabilities @xcite @xcite . by distilling relevant descriptive and review content into concise comparisons, stateof-the-art llm-based contrastive summarization methods such as strum-llm @xcite enable users to comparatively evaluate choices via summaries grounded in concrete data and clear comparisons that are important for decisionmaking @xcite . while these llm-driven approaches arguably improve on their pre-llm predecessors, they often fall short of their contrastive summary potential as we show in our comparative empirical evaluation. this work addresses a critical gap in qcs by leveraging llms to prioritize contrast while maintaining relevance and groundedness as motivated by principles of debate that are founded on discourse theory, including the elaboration likelihood model (elm) @xcite ) and grice's maxims @xcite . building on the state-of-the-art contrastive summarization strum-llm framework @xcite , we propose that aspect-based debate prompting provides a natural framework for improved qcs that we term q-strum debate. an example output summary demonstrating this approach is shown in figure 1 . we summarize our key contributions as follows: street food in bangkok offers an authentic and enriching cultural experience. @xcite melbourne's coffee culture is a notable part of its culinary identity, rooted in italian migration. @xcite bangkok melbourne figure 1 : example of an aspect-based contrastive summary for the query: "culinary cities for food lovers" ## strum for contrastive summarization recommendations often involve presenting users with multiple options, requiring methods that clearly articulate how and why each option aligns with their preferences. strum @xcite introduced a seminal and foundational approach to contrastive summarization by leveraging entailment models and hierarchical clustering to extract, merge, and contrast aspects of data. while effective for structured summarization, strum faced significant limitations in producing outputs that were sufficiently contrastive and aligned with userspecific queries. to address these challenges, strum-llm @xcite integrated large language models (llms) to improve attribute extraction, aspect merging, and summarization. the architecture is outlined in figure 6 (a). strum-llm employs several llm-driven components: while strum-llm provides a state-of-the-art methodology for contrastive summarization, it is not query-driven as originally defined. more critically, we also empirically show that the strum-llm methodology falls short of providing strongly contrastive summaries. to address these gaps, we next introduce q-strum debate, which builds upon strum-llm to deliver query-driven, contrastive summarization. central to this improvement will be the introduction of debate prompting. query: culinary cities for food lovers aspect merge (via llm) bangkok cuisine variety authentic local dishes street food dining experiences melbourne diverse international cuisine rich italian culinary influence merged aspects dining experiences melbourne as a culinary destination unique dining experience cuisine variety rich italian culinary influence diverse cuisine scene vegan diverse international cuisine filter (via llm) bangkok (1) "bangkok boasts a stunning 50k ... [45]" (2) ... (1) "due to the large thai-chinese... [33]" (2) ... diverse cuisine scene global culinary influences melbourne (1) "most italian restaurants will... [6]" (2) ... (1) "for the culinary traveller... [38]" (2) ... unique dining experience diverse cuisine scene (1) "bangkok boasts a... [45]" (10) "a good meal is unlikely to cost more... [45]" (1) "vegetarian food is widely... [45]" (10) "there is an abundance of... [38]" global culinary influences (1) "due to the large... [33]" (10) "even outside chinatown, many of... [33]" (1) "italian cafes... [28]" (10) "many italian style cafes are found... [15]" un ique din ing ex periences (1) "fine dining options... [47]" (10) "many street vendors sell satay... [38]" (1) "eating out is cheaper... [38]" (10) "excellent eateries can be found... [50]" bangkok melbourne query: culinary cities for food lovers diverse cuisine scene figure 4: filter stage for the query: "culinary cities for food lovers" ## experimental design we compare our novel q-strum debate (section 3.3) to the query-driven strum-llm extension q-strum base (section 3.1) and its q-strum contrastive extension (section 3.2) -all using gpt-4o -to address two key research questions: ## experimental results below, we summarize key findings for our previous research questions. experiments used ∼11m tokens of gpt-4o (est. ∼200b parameters (abacha et al., 2025)) api calls and ∼3m tokens of claude-3.5-sonnet (175b parameters) api calls. all code and data to reproduce these results are provided in a public github code repository. foot_1 ## conclusion we introduced a novel debate-style prompting framework called q-strum debate to generate high-quality, query-driven contrastive summaries using llms. we demonstrated that debate-style prompting significantly outperforms baselines, particularly for subjective review datasets, by delivering more contrastive, relevant, diverse, and useful summaries. experiments modulating debate aggressiveness showed marginal impact on results. future directions include multiple entity comparisons, incorporating multi-modal review content, and adapting it to diverse domains such as medical products or hiking trails. further exploration of multi-agent debate methodology could enhance the framework's versatility. overall, these extensions can build on q-strum's novel debate prompting methodology to improve contrastiveness in a variety of query-driven summarization applications.