  Text summarization aims to compress a textual document to a short summary
while keeping salient information. Extractive approaches are widely used in
text summarization because of their fluency and efficiency. However, most of
existing extractive models hardly capture inter-sentence relationships,
particularly in long documents. They also often ignore the effect of topical
information on capturing important contents. To address these issues, this
paper proposes a graph neural network (GNN)-based extractive summarization
model, enabling to capture inter-sentence relationships efficiently via
graph-structured document representation. Moreover, our model integrates a
joint neural topic model (NTM) to discover latent topics, which can provide
document-level features for sentence selection. The experimental results
demonstrate that our model not only substantially achieves state-of-the-art
results on CNN/DM and NYT datasets but also considerably outperforms existing
approaches on scientific paper datasets consisting of much longer documents,
indicating its better robustness in document genres and lengths. Further
discussions show that topical information can help the model preselect salient
contents from an entire document, which interprets its effectiveness in long
document summarization.
