  Clinical conversation summarization has become an important application of
Natural language Processing. In this work, we intend to analyze summarization
model ensembling approaches, that can be utilized to improve the overall
accuracy of the generated medical report called chart note. The work starts
with a single summarization model creating the baseline. Then leads to an
ensemble of summarization models trained on a separate section of the chart
note. This leads to the final approach of passing the generated results to
another summarization model in a multi-layer/stage fashion for better coherency
of the generated text. Our results indicate that although an ensemble of models
specialized in each section produces better results, the multi-layer/stage
approach does not improve accuracy. The code for the above paper is available
at https://github.com/dhananjay-srivastava/MEDIQA-Chat-2023-iuteam1.git
