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arXiv · 2109.01518

Biomedical Data-to-Text Generation via Fine-Tuning Transformers

Ruslan Yermakov·Nicholas Drago·Angelo Ziletti
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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, multisentence 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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Ruslan Yermakov, Nicholas Drago, Angelo Ziletti. 2021-09-03. Biomedical Data-to-Text Generation via Fine-Tuning Transformers. https://arxiv.org/abs/2109.01518

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