@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://preview.aclanthology.org/add-emnlp-2024-awards/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."
}