Year: 2026
Type: conference-paper
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 depends strongly on the strategy and the summarization setting.In particular, we find a trade-off between summary quality and factual faithfulness.Explicit reasoning strategies often improve reference-based quality, but may weaken factual grounding, whereas implicit reasoning in LRMs shows the opposite tendency.We further find that increasing an LRM's internal reasoning budget does not reliably improve summarization and can even reduce factual consistency.These findings suggest that, for summarization, more reasoning is not always better.Effective reasoning should preserve faithful compression rather than induce over-elaboration.Our source code is publicly available.
Authors: Haohan Yuan, Haopeng Zhang
Institution: University of Hawaiʻi at Mānoa
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Science Foundation
Awards: SCH: Personalized Machine Learning for Repeat Adverse Health Events using Novel Multimodal Self-Supervised Pretraining Methods, Collaborative Research: CSSI Frameworks: SAGE3: Smart Amplified Group Environment for Harnessing the Data Revolution, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), RII Track-1: Change Hawaii: Harnessing the Data Revolution for Island Resilience
OpenAlex ID: W4417029943