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Though exponentially growing health-related literature has been madeavailable to a broad audience online, the language of scientific articles canbe difficult for the general public to understand. Therefore, adapting thisexpert-level language into plain language versions is necessary for the publicto reliably comprehend the vast health-related literature. Deep Learningalgorithms for automatic adaptation are a possible solution; however, goldstandard datasets are needed for proper evaluation. Proposed datasets thus farconsist of either pairs of comparable professional- and general public-facingdocuments or pairs of semantically similar sentences mined from such documents.This leads to a trade-off between imperfect alignments and small test sets. Toaddress this issue, we created the Plain Language Adaptation of BiomedicalAbstracts dataset. This dataset is the first manually adapted dataset that isboth document- and sentence-aligned. The dataset contains 750 adaptedabstracts, totaling 7643 sentence pairs. Along with describing the dataset, webenchmark automatic adaptation on the dataset with state-of-the-art DeepLearning approaches, setting baselines for future research.

A Dataset for Plain Language Adaptation of Biomedical Abstracts