用优化BERT生成医学文献摘要，提升信息提取效率。
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study
- 基于BERT微调构建摘要生成系统，提升关键信息提取能力。
- 改进BERT在Rouge和召回率上优于Seq-Seq等模型。
- 适合医学研究者快速筛选海量文献，辅助临床决策。
本文提出一种基于BERT模型的医学文献摘要生成方法，以应对医疗信息爆炸带来的挑战。通过微调与优化BERT模型，构建了一个高效的信息提取与摘要生成系统，可快速从医学文献中抽取关键内容并生成连贯准确的摘要。实验对比了Seq-Seq、Attention、Transformer及BERT等多种模型，结果表明改进后的BERT在Rouge和召回率指标上表现显著更优。此外，研究还验证了知识蒸馏技术对模型性能的进一步提升潜力。该系统在实际应用中展现出良好的通用性与效率，为医学文献的快速筛查与分析提供了可靠工具。
原文摘要 · Abstract (English)
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.
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