Understanding LLM Reasoning for Abstractive Summarization
This work addresses the problem of optimizing reasoning for summarization in NLP, but it is incremental as it refines existing assumptions without introducing new methods.
The study investigated the effectiveness of reasoning strategies in large language models for abstractive summarization, finding that it is not universally beneficial and involves a trade-off between summary quality and factual faithfulness, with explicit reasoning improving fluency but reducing grounding, while implicit reasoning does the opposite.
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.