  Previous approaches for automatic lay summarisation are exclusively reliant
on the source article that, given it is written for a technical audience (e.g.,
researchers), is unlikely to explicitly define all technical concepts or state
all of the background information that is relevant for a lay audience. We
address this issue by augmenting eLife, an existing biomedical lay
summarisation dataset, with article-specific knowledge graphs, each containing
detailed information on relevant biomedical concepts. Using both automatic and
human evaluations, we systematically investigate the effectiveness of three
different approaches for incorporating knowledge graphs within lay
summarisation models, with each method targeting a distinct area of the
encoder-decoder model architecture. Our results confirm that integrating
graph-based domain knowledge can significantly benefit lay summarisation by
substantially increasing the readability of generated text and improving the
explanation of technical concepts.
