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.　©　2023　Association　for　Computational　Linguistics.