Summary of Deep Learning For Medical Text Processing: Bert Model Fine-tuning and Comparative Study, by Jiacheng Hu et al.
Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study
by Jiacheng Hu, Yiru Cang, Guiran Liu, Meiqi Wang, Weijie He, Runyuan Bao
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative approach to summarizing medical literature using the BERT model. By fine-tuning and optimizing this model, the authors develop an efficient system that can quickly extract key information from medical texts and generate accurate summaries. The study compares various models, including Seq-Seq, Attention, Transformer, and BERT, and shows that the improved BERT model excels in Rouge and Recall metrics. Additionally, the results highlight the potential of knowledge distillation techniques to enhance model performance. The system demonstrates strong versatility and efficiency in practical applications, offering a reliable tool for rapid screening and analysis of medical literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help doctors and researchers quickly find important information in huge amounts of medical writing. Right now, there’s too much information out there, and it can be hard to find what you need. The authors created a new way to use the BERT model to summarize this information and make it easier to understand. They tested different models and found that their improved BERT model is the best at getting the important details right. This new system can help doctors and researchers quickly find what they need, making it a valuable tool for the medical community. |
Keywords
» Artificial intelligence » Attention » Bert » Fine tuning » Knowledge distillation » Recall » Rouge » Transformer
