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