Résumé:
This study is situated within the context of the digital transformation of medical records and aims to automate the summarization of clinical notes to support clinical decision-making. The main objective is to develop a model capable of generating coherent and factual summaries from the discharge summaries of the MIMIC-IV dataset. The adopted methodology is based on the fine-tuning of the LongT5 model, chosen for its ability to process very long text sequences. Particular attention was given to data preproces- sing, notably the generation of coherent and precise target summaries to ensure high-quality supervision. The adaptation of the pre-trained model was then efficiently performed using the LoRA (Low-Rank Adaptation) method. Our final model, named MedSum-LongT5, achieves solid performance with scores of 52.6% for ROUGE-1, 35.3% for ROUGE-2, and 42.9% for ROUGE-L. These results signifi- cantly outperform non-specialized baseline models, validating the effectiveness of the approach. Qualitative analysis conf
