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 https://github.com/MoLICHENXI/HAESum

我们提出了 HAESum，一种利用图神经网络在基于分层话语结构的文档中进行局部和全局建模的新方法。通过使用局部异构图来学习句内关系，然后引入一种新颖的超图自注意层以进一步增强句间高阶关系的表征，在两个基准数据集上验证了我们的方法，实验结果表明考虑分层结构在建模长篇科技文档中的重要性以及 HAESum 的有效性。