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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.20792 |
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| _version_ | 1866912089584435200 |
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| author | Hu, Jiacheng Cang, Yiru Liu, Guiran Wang, Meiqi He, Weijie Bao, Runyuan |
| author_facet | Hu, Jiacheng Cang, Yiru Liu, Guiran Wang, Meiqi He, Weijie Bao, Runyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20792 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study Hu, Jiacheng Cang, Yiru Liu, Guiran Wang, Meiqi He, Weijie Bao, Runyuan Computation and Language Machine Learning 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. |
| title | Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.20792 |