Biomedical Data-to-Text Generation via Fine-Tuning Transformers
Ruslan Yermakov, Nicholas Drago, Angelo Ziletti · 2021
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.