Heterogeneous Graph Neural Networks for Extractive Document Summarization
Danqing Wang, Pengfei Liu, Yining Zheng, Xipeng Qiu, Xuanjing Huang · 2020
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 graphbased 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 singledocument 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 1 .