Large language models (LLMs) are increasingly used to summarize academic work, yet model summaries can subtly exaggerate or mischaracterize findings.We examine how Narrative License (NL), rhetorical shifts that amplify claims beyond the underlying evidence, emerges in LLM summaries of scholarly articles.Using diverse prompting strategies across six leading models, we assess three dimensions of NL: causal overreach, rhetorical confidence, and sentiment (N = 100 peer-reviewed articles).Under basic summarization prompts, models frequently increase NL relative to academic abstracts; however, guardrail prompts can reduce these distortions.We further test how model "sycophancy" shapes NL, finding that stated stances and user personas produce predictable shifts in each element.These findings suggest that users and the benchmarks used to evaluate summarization should explicitly consider subtle rhetorical distortions and user alignment to ensure faithful scientific communication.