CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models

Potsawee Manakul, Yassir Fathullah, Adian Liusie, Vyas Raina, Vatsal Raina +1 more
6/8/2023
cs.CL

No official code found. Best implementation: potsawee/hierarchical_ensemble_summ (3 stars, MIT, last commit 2023-08-09).

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 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.

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Code Implementations(1)

potsawee/hierarchical_ensemble_summCommunity100%
MIT

Hierarchical Ensemble of Summarization Models

30Python, Jupyter NotebookJun 5, 20233 years agoMIT

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Cite this paper

@article{manakul2023cued,
  title  = {CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models},
  author = {Potsawee Manakul and Yassir Fathullah and Adian Liusie and Vyas Raina and Vatsal Raina and Mark Gales},
  year   = {2023},
  eprint = {2306.05317},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.05317v1}
}

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