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

IUTEAM1 at MEDIQA-Chat 2023: Is simple fine tuning effective for multilayer summarization of clinical conversations?