A Dataset for Plain Language Adaptation of Biomedical Abstracts

TL;DR

This paper introduces a novel, manually aligned dataset of biomedical abstracts adapted into plain language, enabling better evaluation of deep learning models for health communication.

Contribution

The creation of the first document- and sentence-aligned dataset for biomedical abstract adaptation into plain language, facilitating improved model training and evaluation.

Findings

Benchmarking with state-of-the-art models establishes baseline performance.

The dataset contains 750 abstracts with 7643 sentence pairs.

Provides a resource for future research in biomedical plain language adaptation.

Abstract

Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature. Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed for proper evaluation. Proposed datasets thus far consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This leads to a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical Abstracts dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark automatic adaptation on the dataset with state-of-the-art Deep Learning approaches, setting baselines for future research.