  Document-level relation extraction (DocRE) aims to extract relations between
entities from unstructured document text. Compared to sentence-level relation
extraction, it requires more complex semantic understanding from a broader text
context. Currently, some studies are utilizing logical rules within evidence
sentences to enhance the performance of DocRE. However, in the data without
provided evidence sentences, researchers often obtain a list of evidence
sentences for the entire document through evidence retrieval (ER). Therefore,
DocRE suffers from two challenges: firstly, the relevance between evidence and
entity pairs is weak; secondly, there is insufficient extraction of complex
cross-relations between long-distance multi-entities. To overcome these
challenges, we propose GEGA, a novel model for DocRE. The model leverages graph
neural networks to construct multiple weight matrices, guiding attention
allocation to evidence sentences. It also employs multi-scale representation
aggregation to enhance ER. Subsequently, we integrate the most efficient
evidence information to implement both fully supervised and weakly supervised
training processes for the model. We evaluate the GEGA model on three widely
used benchmark datasets: DocRED, Re-DocRED, and Revisit-DocRED. The
experimental results indicate that our model has achieved comprehensive
improvements compared to the existing SOTA model.
