  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
