  In recent years, the trend of deploying digital systems in numerous
industries has hiked. The health sector has observed an extensive adoption of
digital systems and services that generate significant medical records.
Electronic health records contain valuable information for prospective and
retrospective analysis that is often not entirely exploited because of the
complicated 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 a reported disease. These summaries may boost
diagnosis and save a doctor's time during a saturated workload situation like
the COVID-19 pandemic. In this paper, we are applying a multi-head
attention-based mechanism to perform extractive summarization of meaningful
phrases on clinical notes. Our method finds major sentences for a summary by
correlating tokens, segments, and positional embeddings of sentences in a
clinical note. The model outputs attention scores that are statistically
transformed to extract critical phrases for visualization on the heat-mapping
tool and for human use.
