Biomedical Text Summarization for Medical Insights

With a vast increase in the present amount of digital information with regards to biomedical research, processing related data in an efficient manner assumes critical importance. This work proposes a hybrid summarization method combining extractive and abstractive techniques with the help of cutting-edge models in NLP. While BERT is used to select the most informative sentences capable of summarizing main ideas in a document, Pegasus has been used to generate smooth, concise summaries for the purpose. The results with the metrics like Cosine Similarity and BERTScore, incorporating precision, recall, and F1 score, show the system proposed performs very well by holding onto the meaning that preserves coherent summaries of high quality on generation. It is of great benefit to researchers as well as professionals regarding rapid extraction from vast amounts of complex data.