As a crucial step in extractive document summarization, learning
cross-sentence relations has been explored by a plethora of approaches. An
intuitive way is to put them in the graph-based neural network, which has a
more complex structure for capturing inter-sentence relationships. In this
paper, we present a heterogeneous graph-based neural network for extractive
summarization (HeterSumGraph), which contains semantic nodes of different
granularity levels apart from sentences. These additional nodes act as the
intermediary between sentences and enrich the cross-sentence relations.
Besides, our graph structure is flexible in natural extension from a
single-document setting to multi-document via introducing document nodes. To
our knowledge, we are the first one to introduce different types of nodes into
graph-based neural networks for extractive document summarization and perform a
comprehensive qualitative analysis to investigate their benefits. The code will
be released on Github

本文介绍了一种异构图神经网络 (HeterSumGraph) 用于信息抽取式文档摘要的任务中，该网络结构包含不同粒度级别的语义节点，这些额外的节点作为句子之间的中介，丰富交叉句关系，同时我们的图结构在自然扩展从单个文档设置到 多文档，引入文档节点。该方法是第一次在基于图的神经网络中引入不同类型的节点进行信息抽取式文档摘要，并进行了全面的定性分析以探究其优劣。