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。

利用指针 GPT 的生物医学文本摘要的最佳路径

Optimal path for Biomedical Text Summarization Using Pointer GPT

The exponential growth of biomedical texts such as biomedical literature and
electronic health records (EHRs), provides a big challenge for clinicians and
researchers to access clinical information efficiently. To address the problem,
biomedical text summarization has been proposed to support clinical information
retrieval and management, aiming at generating concise summaries that distill
key information from single or multiple biomedical documents. In recent years,
pre-trained language models (PLMs) have been the de facto standard of various
natural language processing tasks in the general domain. Most recently, PLMs
have been further investigated in the biomedical field and brought new insights
into the biomedical text summarization task. In this paper, we systematically
summarize recent advances that explore PLMs for biomedical text summarization,
to help understand recent progress, challenges, and future directions. We
categorize PLMs-based approaches according to how they utilize PLMs and what
PLMs they use. We then review available datasets, recent approaches and
evaluation metrics of the task. We finally discuss existing challenges and
promising future directions. To facilitate the research community, we line up
open resources including available datasets, recent approaches, codes,
evaluation metrics, and the leaderboard in a public project:
this https URL

本文系统总结了使用预训练语言模型（ Pre-trained language models）的生物医学文本摘要的最新进展、挑战问题和未来方向，帮助我们更好地理解该领域的最新进展和使用预训练语言模型在生物信息学中的应用。