@inproceedings{yermakov-etal-2021-biomedical,
title = "Biomedical Data-to-Text Generation via Fine-Tuning Transformers",
author = "Yermakov, Ruslan and
Drago, Nicholas and
Ziletti, Angelo",
editor = "Belz, Anya and
Fan, Angela and
Reiter, Ehud and
Sripada, Yaji",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.inlg-1.40",
doi = "10.18653/v1/2021.inlg-1.40",
pages = "364--370",
abstract = "Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.",
}
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%0 Conference Proceedings
%T Biomedical Data-to-Text Generation via Fine-Tuning Transformers
%A Yermakov, Ruslan
%A Drago, Nicholas
%A Ziletti, Angelo
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F yermakov-etal-2021-biomedical
%X Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.
%R 10.18653/v1/2021.inlg-1.40
%U https://aclanthology.org/2021.inlg-1.40
%U https://doi.org/10.18653/v1/2021.inlg-1.40
%P 364-370
Markdown (Informal)
[Biomedical Data-to-Text Generation via Fine-Tuning Transformers](https://aclanthology.org/2021.inlg-1.40) (Yermakov et al., INLG 2021)
ACL