  Scientific document summarization has been a challenging task due to the long
structure of the input text. The long input hinders the simultaneous effective
modeling of both global high-order relations between sentences and local
intra-sentence relations which is the most critical step in extractive
summarization. However, existing methods mostly focus on one type of relation,
neglecting the simultaneous effective modeling of both relations, which can
lead to insufficient learning of semantic representations. In this paper, we
propose HAESum, a novel approach utilizing graph neural networks to locally and
globally model documents based on their hierarchical discourse structure.
First, intra-sentence relations are learned using a local heterogeneous graph.
Subsequently, a novel hypergraph self-attention layer is introduced to further
enhance the characterization of high-order inter-sentence relations. We
validate our approach on two benchmark datasets, and the experimental results
demonstrate the effectiveness of HAESum and the importance of considering
hierarchical structures in modeling long scientific documents. Our code will be
available at \url{https://github.com/MoLICHENXI/HAESum}
