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}

翻译：科学文档摘要因输入文本篇幅较长而具有挑战性。长文本输入阻碍了同时有效建模句子间的全局高阶关系及句子内的局部关系，而这是抽取式摘要中最关键的一步。然而，现有方法大多仅关注单一类型的关系，忽略了同时有效建模这两种关系，这可能导致语义表示学习不充分。本文提出HAESum，一种利用图神经网络基于文档层次化篇章结构进行局部与全局建模的新方法。首先，通过局部异构图学习句子内关系。随后，引入新颖的超图自注意力层，以进一步增强对句子间高阶关系的表征。我们在两个基准数据集上验证了该方法，实验结果表明了HAESum的有效性，以及在建模长科学文档时考虑层次化结构的重要性。我们的代码将发布于\url{https://github.com/MoLICHENXI/HAESum}