@inproceedings{navarro-etal-2022-shot,
title = "Few-shot fine-tuning {SOTA} summarization models for medical dialogues",
author = "Navarro, David Fraile and
Dras, Mark and
Berkovsky, Shlomo",
editor = "Ippolito, Daphne and
Li, Liunian Harold and
Pacheco, Maria Leonor and
Chen, Danqi and
Xue, Nianwen",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-srw.32",
doi = "10.18653/v1/2022.naacl-srw.32",
pages = "254--266",
abstract = "Abstractive summarization of medical dialogues presents a challenge for standard training approaches, given the paucity of suitable datasets. We explore the performance of state-of-the-art models with zero-shot and few-shot learning strategies and measure the impact of pretraining with general domain and dialogue-specific text on the summarization performance.",
}
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%0 Conference Proceedings
%T Few-shot fine-tuning SOTA summarization models for medical dialogues
%A Navarro, David Fraile
%A Dras, Mark
%A Berkovsky, Shlomo
%Y Ippolito, Daphne
%Y Li, Liunian Harold
%Y Pacheco, Maria Leonor
%Y Chen, Danqi
%Y Xue, Nianwen
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F navarro-etal-2022-shot
%X Abstractive summarization of medical dialogues presents a challenge for standard training approaches, given the paucity of suitable datasets. We explore the performance of state-of-the-art models with zero-shot and few-shot learning strategies and measure the impact of pretraining with general domain and dialogue-specific text on the summarization performance.
%R 10.18653/v1/2022.naacl-srw.32
%U https://aclanthology.org/2022.naacl-srw.32
%U https://doi.org/10.18653/v1/2022.naacl-srw.32
%P 254-266
Markdown (Informal)
[Few-shot fine-tuning SOTA summarization models for medical dialogues](https://aclanthology.org/2022.naacl-srw.32) (Navarro et al., NAACL 2022)
ACL
- David Fraile Navarro, Mark Dras, and Shlomo Berkovsky. 2022. Few-shot fine-tuning SOTA summarization models for medical dialogues. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, pages 254–266, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.