Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study

TL;DR

This paper develops a BERT-based system for summarizing medical literature, demonstrating superior performance over other models and highlighting the potential of knowledge distillation for further improvements.

Contribution

It introduces a fine-tuned BERT model tailored for medical text summarization and compares its effectiveness with other neural models, emphasizing its practical utility.

Findings

BERT outperforms Seq-Seq, Attention, and Transformer models in Rouge and Recall metrics.

Knowledge distillation further enhances model performance.

The system is efficient and versatile for medical literature analysis.

Abstract

This paper proposes a medical literature summary generation method based on the BERT model to address the challenges brought by the current explosion of medical information. By fine-tuning and optimizing the BERT model, we develop an efficient summary generation system that can quickly extract key information from medical literature and generate coherent, accurate summaries. In the experiment, we compared various models, including Seq-Seq, Attention, Transformer, and BERT, and demonstrated that the improved BERT model offers significant advantages in the Rouge and Recall metrics. Furthermore, the results of this study highlight the potential of knowledge distillation techniques to further enhance model performance. The system has demonstrated strong versatility and efficiency in practical applications, offering a reliable tool for the rapid screening and analysis of medical literature.