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

arXiv: https://arxiv.org/abs/2109.01518 · HTML: https://mlsanity.com/paper/2109.01518/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance](https://mlsanity.com/paper/1909.02622/) (2019)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [The E2E Dataset: New Challenges For End-to-End Generation](https://mlsanity.com/paper/1706.09254/) (2017)
- [Why We Need New Evaluation Metrics for NLG](https://mlsanity.com/paper/1707.06875/) (2017)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Data-to-Text Generation with Content Selection and Planning](https://mlsanity.com/paper/1809.00582/) (2018)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [A Human Evaluation of AMR-to-English Generation Systems](https://mlsanity.com/paper/2004.06814/) (2020)
- [Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service](https://mlsanity.com/paper/1910.07419/) (2019)

## Built on by (most cited first, 9 of 9)

- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator](https://mlsanity.com/paper/2402.09742/) (2024)
- [Generating Medical Prescriptions with Conditional Transformer](https://mlsanity.com/paper/2310.19727/) (2023)
- [VTechAGP: An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models](https://mlsanity.com/paper/2411.04825/) (2024)
- [AITA Generating Moral Judgements of the Crowd with Reasoning](https://mlsanity.com/paper/2310.18336/) (2023)
- [An Extensive Evaluation of Factual Consistency in Large Language Models for Data-to-Text Generation](https://mlsanity.com/paper/2411.19203/) (2024)
- [Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation](https://mlsanity.com/paper/2407.14088/) (2024)
- [Cloning Ideology and Style using Deep Learning](https://mlsanity.com/paper/2211.07712/) (2022)
- [Exploring Post-Training Alignment of Small Language Models for Biomedical Data-to-Text Generation: A Case Study of Medication Leaflet](https://mlsanity.com/paper/2607.13430/) (2026)

## Abstract (arXiv)

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.