  Long document question answering is a challenging task due to its demands for
complex reasoning over long text. Previous works usually take long documents as
non-structured flat texts or only consider the local structure in long
documents. However, these methods usually ignore the global structure of the
long document, which is essential for long-range understanding. To tackle this
problem, we propose Compressive Graph Selector Network (CGSN) to capture the
global structure in a compressive and iterative manner. The proposed model
mainly focuses on the evidence selection phase of long document question
answering. Specifically, it consists of three modules: local graph network,
global graph network and evidence memory network. Firstly, the local graph
network builds the graph structure of the chunked segment in token, sentence,
paragraph and segment levels to capture the short-term dependency of the text.
Secondly, the global graph network selectively receives the information of each
level from the local graph, compresses them into the global graph nodes and
applies graph attention to the global graph nodes to build the long-range
reasoning over the entire text in an iterative way. Thirdly, the evidence
memory network is designed to alleviate the redundancy problem in the evidence
selection by saving the selected result in the previous steps. Extensive
experiments show that the proposed model outperforms previous methods on two
datasets.
