Selective Attention Encoders by Syntactic Graph Convolutional Networks for Document Summarization
Table 1: Results of abstractive summarizers on the CNN-DM dataset. The first section shows extractive baselines. The second section describes abstractive approaches. The third section presents our model. All our ROUGE scores have a 95% confidence interval with at most 0.25.
['Methods', 'R-1', 'R-2', 'R-L']
Comparing with the same architecture of Coverage, our model has significant improvement on (2.26 on ROUGE-1, 1.78 on ROUGE-2, 2.18 on ROUGE-L), which demonstrates the effectiveness of our model combining the syntactic and semantic information by information gate and modeling the heterogeneous document-level graph via stacked GCNs.