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Document-level event extraction (DEE) faces two main challenges:arguments-scattering and multi-event. Although previous methods attempt toaddress these challenges, they overlook the interference of event-unrelatedsentences during event detection and neglect the mutual interference ofdifferent event roles during argument extraction. Therefore, this paperproposes a novel Schema-based Explicitly Aggregating~(SEA) model to addressthese limitations. SEA aggregates event information into event type and rolerepresentations, enabling the decoding of event records based on specifictype-aware representations. By detecting each event based on its event typerepresentation, SEA mitigates the interference caused by event-unrelatedinformation. Furthermore, SEA extracts arguments for each role based on itsrole-aware representations, reducing mutual interference between differentroles. Experimental results on the ChFinAnn and DuEE-fin datasets show that SEAoutperforms the SOTA methods.

Type-aware Decoding via Explicitly Aggregating Event Information for Document-level Event Extraction