  Biomedical text summarization is a critical tool that enables clinicians to
effectively ascertain patient status. Traditionally, text summarization has
been accomplished with transformer models, which are capable of compressing
long documents into brief summaries. However, transformer models are known to
be among the most challenging natural language processing (NLP) tasks.
Specifically, GPT models have a tendency to generate factual errors, lack
context, and oversimplify words. To address these limitations, we replaced the
attention mechanism in the GPT model with a pointer network. This modification
was designed to preserve the core values of the original text during the
summarization process. The effectiveness of the Pointer-GPT model was evaluated
using the ROUGE score. The results demonstrated that Pointer-GPT outperformed
the original GPT model. These findings suggest that pointer networks can be a
valuable addition to EMR systems and can provide clinicians with more accurate
and informative summaries of patient medical records. This research has the
potential to usher in a new paradigm in EMR systems and to revolutionize the
way that clinicians interact with patient medical records.
