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

TL;DR

This paper demonstrates that fine-tuned transformer models can generate realistic biomedical package leaflets from data, introduces a new dataset for benchmarking, and discusses current limitations in biomedical data-to-text generation.

Contribution

It presents a novel application of transformers to biomedical data-to-text generation and releases a new dataset for future research in this area.

Findings

Transformers can generate realistic biomedical texts from data.

The models have notable limitations in the biomedical domain.

A new dataset, BioLeaflets, is introduced for benchmarking.

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.