  Document-level relation extraction typically relies on text-based encoders
and hand-coded pooling heuristics to aggregate information learned by the
encoder. In this paper, we leverage the intrinsic graph processing capabilities
of the Transformer model and propose replacing hand-coded pooling methods with
new tokens in the input, which are designed to aggregate information via
explicit graph relations in the computation of attention weights. We introduce
a joint text-graph Transformer model and a graph-assisted declarative pooling
(GADePo) specification of the input, which provides explicit and high-level
instructions for information aggregation. GADePo allows the pooling process to
be guided by domain-specific knowledge or desired outcomes but still learned by
the Transformer, leading to more flexible and customisable pooling strategies.
We evaluate our method across diverse datasets and models and show that our
approach yields promising results that are consistently better than those
achieved by the hand-coded pooling functions.
