The discharge summary is an important document, summarizing a patient’s medical information during their hospital stay. It is crucial for communication between clinicians and primary care physicians. Creating a discharge sum- mary is a necessary task. However, it is time-consuming for physicians. Using technology to automatically generate discharge summaries can be helpful for physicians and assist them in concentrating more on the patients than writing clinical summarization notes and discharge summaries. This master’s thesis aims to contribute to the research of building a transformer-based model for an automated discharge summary with a pre-trained DistilBART language model. This study plans to answer this main research question: How e↵ective is the pre-trained DistilBART language model in predicting an automated discharge summary for multiple clinical texts? The research strategy used in this study is experimental. the dataset is MIMIC- III. To evaluate the e↵ectiveness of the model, ROUGE scores are selected. The result of this model is compared with the result of the baseline BART model, which is implemented on the same dataset in the other recent research. This study regards multiple document summarization as the process of combining multiple inputs into a single input, which is then summarized. The findings indicate an improvement in ROUGE-2 and ROUGE-Lsum in the DistilBART model in comparison with the baseline BART model. However, one important limitation was computational resource constraint. The study also provides eth- ical considerations and some recommendations for future works.