Understanding LLM Reasoning for Abstractive Summarization

2025-12-03

AI Summary

This study investigates the effectiveness and limitations of reasoning capabilities in large language models (LLMs) for abstractive summarization. Addressing the underexamined hypothesis that “reasoning universally improves summary quality,” we systematically evaluate eight explicit and implicit reasoning strategies—including chain-of-thought, self-consistency, and stepwise reasoning—across three prominent reasoning-oriented LLMs and eight diverse benchmark datasets. Results reveal a critical trade-off: explicit reasoning enhances linguistic fluency but degrades factual consistency, whereas implicit reasoning exhibits the opposite pattern; moreover, increasing internal reasoning steps does not consistently improve performance. Our core contribution is the empirical identification of an inherent tension between summary quality and factual fidelity in abstractive summarization, leading to the principle that “faithful compression outweighs excessive reasoning.” This work provides both empirical evidence and methodological guidance for the principled design and deployment of reasoning mechanisms in LLM-based summarization systems.

Technology Category

• Natural Language Processing: Summarization
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

While the reasoning capabilities of Large Language Models (LLMs) excel in analytical tasks such as mathematics and code generation, their utility for abstractive summarization remains widely assumed but largely unverified. To bridge this gap, we first tailor general reasoning strategies to the summarization domain. We then conduct a systematic, large scale comparative study of 8 reasoning strategies and 3 Large Reasoning Models (LRMs) across 8 diverse datasets, assessing both summary quality and faithfulness. Our findings show that reasoning is not a universal solution and its effectiveness is highly dependent on the specific strategy and context. Specifically, we observe a trade-off between summary quality and factual faithfulness: explicit reasoning strategies tend to improve fluency at the expense of factual grounding, while implicit reasoning in LRMs exhibits the inverse pattern. Furthermore, increasing an LRM's internal reasoning budget does not improve, and can even hurt, factual consistency, suggesting that effective summarization demands faithful compression rather than creative over-thinking.

Problem

• Evaluates LLM reasoning strategies for abstractive summarization effectiveness
• Analyzes trade-off between summary quality and factual faithfulness in reasoning
• Investigates how reasoning budget impacts factual consistency in summarization

Innovation

• Tailored reasoning strategies for summarization
• Systematic comparative study of reasoning strategies
• Explicit vs implicit reasoning trade-off analysis