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Lay summarisation aims to jointly summarise and simplify a given text, thusmaking its content more comprehensible to non-experts. Automatic approaches forlay summarisation can provide significant value in broadening access toscientific literature, enabling a greater degree of both interdisciplinaryknowledge sharing and public understanding when it comes to research findings.However, current corpora for this task are limited in their size and scope,hindering the development of broadly applicable data-driven approaches. Aimingto rectify these issues, we present two novel lay summarisation datasets, PLOS(large-scale) and eLife (medium-scale), each of which contains biomedicaljournal articles alongside expert-written lay summaries. We provide a thoroughcharacterisation of our lay summaries, highlighting differing levels ofreadability and abstractiveness between datasets that can be leveraged tosupport the needs of different applications. Finally, we benchmark our datasetsusing mainstream summarisation approaches and perform a manual evaluation withdomain experts, demonstrating their utility and casting light on the keychallenges of this task.

Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature