  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.
