The trend of deploying digital systems in numerous industries has induced a hike in recording digital information . The health sector has observed a large adoption of digital devices and systems generating large volumes of personal medical health records . Electronic health records contain valuable information for retrospective and prospective analysis that is often not entirely exploited because of the dense information storage . The crude purpose of condensing health records is to select the information that holds most characteristics of the original documents based on reported disease . These summaries may boost diagnosis and extend a doctor's interaction time with the patient during a high workload situation like the COVID-19 pandemic . In this paper, we propose a multi-head attention-based mechanism to perform extractive summarization of meaningful phrases in clinical notes . This method finds major sentences for a summary by correlating tokens, segments and positional embeddings . The model outputs attention scores that are statistically transformed to extract key phrases and can be used for a projection on the heat-mapping tool for visual and human use.