In recent years, significant technological advances have been driven by the emergence of LLMs (Large Language Models). Among the sectors that stand to benefit most from these developments, healthcare is particularly noteworthy due to its direct im
pact on society. More specifically, clinical text summarization has become an increasingly important task, given the large volume of clinical documents generated daily and the value of concise summaries for improving information comprehension and dissemination.
In this context, this work proposes a robust and accessible methodology for clinical text summarization while presenting an open weights model with strong potential for addressing this task effectively.