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In this paper, we consider the challenge of summarizing patients' medicalprogress notes in a limited data setting. For the Problem List Summarization(shared task 1A) at the BioNLP Workshop 2023, we demonstrate that Clinical-T5fine-tuned to 765 medical clinic notes outperforms other extractive,abstractive and zero-shot baselines, yielding reasonable baseline systems formedical note summarization. Further, we introduce Hierarchical Ensemble ofSummarization Models (HESM), consisting of token-level ensembles of diversefine-tuned Clinical-T5 models, followed by Minimum Bayes Risk (MBR) decoding.Our HESM approach lead to a considerable summarization performance boost, andwhen evaluated on held-out challenge data achieved a ROUGE-L of 32.77, whichwas the best-performing system at the top of the shared task leaderboard.

CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models