CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models

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

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