@inproceedings{manakul-etal-2023-cued,
    title = "{CUED} at {P}rob{S}um 2023: Hierarchical Ensemble of Summarization Models",
    author = "Manakul, Potsawee  and
      Fathullah, Yassir  and
      Liusie, Adian  and
      Raina, Vyas  and
      Raina, Vatsal  and
      Gales, Mark",
    editor = "Demner-fushman, Dina  and
      Ananiadou, Sophia  and
      Cohen, Kevin",
    booktitle = "The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bionlp-1.51",
    doi = "10.18653/v1/2023.bionlp-1.51",
    pages = "516--523",
    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.",
}

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.
Manakul, Potsawee Fathullah, Yassir Liusie, Adian Raina, Vyas Raina, Vatsal Gales, Mark
CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models