  Extractive summarization for long documents is challenging due to the
extended structured input context. The long-distance sentence dependency
hinders cross-sentence relations modeling, the critical step of extractive
summarization. This paper proposes HEGEL, a hypergraph neural network for long
document summarization by capturing high-order cross-sentence relations. HEGEL
updates and learns effective sentence representations with hypergraph
transformer layers and fuses different types of sentence dependencies,
including latent topics, keywords coreference, and section structure. We
validate HEGEL by conducting extensive experiments on two benchmark datasets,
and experimental results demonstrate the effectiveness and efficiency of HEGEL.
