Understanding LLMs' summarization capabilities: an analysis of biomedical abstract and lay summary generation

Scientific abstracts and lay summaries serve distinct but critical roles in research communication.Abstracts use technical language for academic audiences, while lay summaries aim to make findings accessible to nonspecialists.With the rise of large language models (LLMs), there is increasing interest in automating the generation of both types of summaries-especially in the biomedical domain, where clarity and factual accuracy are essential.This study evaluates the performance of lightweight LLMs (under 8B parameters) in generating biomedical abstracts and lay summaries in a zero-shot setting.We assess outputs across three key dimensions: relevance, readability, and factuality.Additionally, we introduce a novel analysis of the sectional origin and desirability of information-where desirability reflects the utility of content from the reader's perspective.We further compare human and LLM preferences using an objective ranking task.Our results show that LLM-generated summaries often contain comparable levels of desirable information to gold-standard human references.In several cases, LLM outputs are preferred by human evaluators and occasionally mistaken for human-authored text.These findings demonstrate the potential of lightweight LLMs for scalable, high-quality summarization and suggest their practical use in domains requiring both technical and accessible communication.The codebase for this study is publicly available onGitHub: https://github.com/b atuinmetz/Understanding