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.

通过使用指针网络替代原始 GPT 模型的注意力机制，研究发现指针 - GPT 模型在生物医学文本摘要中优于原始 GPT 模型，这对于电子病历系统来说是具有价值的重要补充，可以为临床医生提供更准确、更丰富的病历摘要信息，有可能在电子病历系统中开创一种新的模式并改变临床医生与病历交互方式。