  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.
