Due to the unique nature of the COVID-19 pandemic, text data related to it has exhibited explosive growth in scale within a short period. This data is characterized by its diversity and significant variations. To comprehensively study this textual information and better understand the virus’s development trends and mutation directions, automatic summarization techniques play a crucial role. In this project, a fine-tuned BERT model is applied to generate medical text summaries based on publicly available literature data.