  Sequential abstractive neural summarizers often do not use the underlying
structure in the input article or dependencies between the input sentences.
This structure is essential to integrate and consolidate information from
different parts of the text. To address this shortcoming, we propose a
hierarchy-aware graph neural network (HierGNN) which captures such dependencies
through three main steps: 1) learning a hierarchical document structure through
a latent structure tree learned by a sparse matrix-tree computation; 2)
propagating sentence information over this structure using a novel
message-passing node propagation mechanism to identify salient information; 3)
using graph-level attention to concentrate the decoder on salient information.
Experiments confirm HierGNN improves strong sequence models such as BART, with
a 0.55 and 0.75 margin in average ROUGE-1/2/L for CNN/DM and XSum. Further
human evaluation demonstrates that summaries produced by our model are more
relevant and less redundant than the baselines, into which HierGNN is
incorporated. We also find HierGNN synthesizes summaries by fusing multiple
source sentences more, rather than compressing a single source sentence, and
that it processes long inputs more effectively.
