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    <abstract>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 ClinicalT5 fine-tuned to 765 medical clinic notes outperforms other extractive, abstractive and zero-shot baselines, yielding reasonable baseline systems for medical note summarization. Further, we introduce Hierarchical Ensemble of Summarization Models (HESM), consisting of token-level ensembles of diverse fine-tuned ClinicalT5 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.</abstract>
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%0 Conference Proceedings
%T CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
%A Manakul, Potsawee
%A Fathullah, Yassir
%A Liusie, Adian
%A Raina, Vyas
%A Raina, Vatsal
%A Gales, Mark
%Y Demner-fushman, Dina
%Y Ananiadou, Sophia
%Y Cohen, Kevin
%S The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F manakul-etal-2023-cued
%X 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 ClinicalT5 fine-tuned to 765 medical clinic notes outperforms other extractive, abstractive and zero-shot baselines, yielding reasonable baseline systems for medical note summarization. Further, we introduce Hierarchical Ensemble of Summarization Models (HESM), consisting of token-level ensembles of diverse fine-tuned ClinicalT5 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.
%R 10.18653/v1/2023.bionlp-1.51
%U https://aclanthology.org/2023.bionlp-1.51
%U https://doi.org/10.18653/v1/2023.bionlp-1.51
%P 516-523

Markdown (Informal)

[CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models](https://aclanthology.org/2023.bionlp-1.51) (Manakul et al., BioNLP 2023)

• CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models (Manakul et al., BioNLP 2023)

ACL

• Potsawee Manakul, Yassir Fathullah, Adian Liusie, Vyas Raina, Vatsal Raina, and Mark Gales. 2023. CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 516–523, Toronto, Canada. Association for Computational Linguistics.