Abstract:
In the current era of information overload, summarizing biomedical literature
has become crucial. The rapid increase in research publications challenges
researchers, clinicians, and policymakers to stay updated on the latest
findings. This study evaluates the performance of BioBERT, a large language
model (LLM), in summarizing biomedical papers from PubMed. BioBERT‟s
performance was measured using BERTScore, yielding impressive results:
an F1 score of 92%, with precision and recall of 88% and 87%, respectively.
These metrics underscore BioBERT‟s proficiency in generating coherent and
accurate summaries of complex biomedical texts. A comparative analysis
with other models, including ClinicalBioBERT, SciBERT, and BERT,
showed that BioBERT outperformed its counterparts, particularly in
understanding domain-specific language and capturing critical information
efficiently. BioBERT‟s superior performance positions it as a powerful tool
for condensing vast biomedical literature into concise summaries. The
findings contribute to ongoing research on the applications of LLMs in
healthcare and bioinformatics. By demonstrating BioBERT‟s effectiveness in
summarization tasks, this study highlights the potential of LLMs to
streamline information processing, enabling quicker and more accurate
dissemination of knowledge. These results emphasize the transformative role
of models like BioBERT in the biomedical domain, facilitating better use of
research findings and promoting more informed decisions in healthcare.
Finally, this study encourages further exploration of LLMs' capabilities in
specialized tasks, such as information retrieval and knowledge extraction.
