arXiv:2512.03503v3 Announce Type: replace Abstract: Reasoning has substantially improved Large Language Models (LLMs) on analytical tasks such as mathematics and code generation, but its value for abstractive summarization remains unclear. To address this gap, we adapt general reasoning strategies to the summarization setting and conduct a large-scale comparative study of 8 reasoning strategies and 3 Large Reasoning Models (LRMs) across 8 diverse datasets, evaluating both summary quality and factual faithfulness. Our results show that reasoning is not a universal solution and its effectiveness
The proliferation of LLMs and increasing investment in their capabilities makes understanding their limitations and optimal application crucial for practical deployment and further research.
Sophisticated readers need to discern where LLM 'reasoning' truly adds value versus where it is merely an overhead, impacting investment and development strategies in AI applications.
This research refines the understanding of abstractive summarization methods, indicating that simply applying 'reasoning' to LLMs does not universally guarantee better outcomes.
- · AI researchers focusing on targeted LLM improvement
- · Developers optimizing resource allocation for specific AI tasks
- · Users seeking efficient and factually faithful summarization tools
- · Companies investing broadly in 'reasoning' modules without specific use-case val
- · LLM developers overpromising universal reasoning capabilities
The study clarifies the effectiveness of different reasoning strategies for LLMs in abstractive summarization.
It could lead to more targeted and efficient development of LLM applications, avoiding wasted computational resources on ineffective reasoning methods.
This refinement might influence the design of future LLM architectures, emphasizing task-specific reasoning mechanisms over general-purpose ones.
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Read at arXiv cs.CL