DivGraphPointer
Table 3. Effectiveness of encoding documents as graphs with graph convolutional neural networks.
['[EMPTY]', '[BOLD] Inspec  [BOLD] F1@5', '[BOLD] Inspec  [BOLD] F1@10', '[BOLD] Krapivin  [BOLD] F1@5', '[BOLD] Krapivin  [BOLD] F1@10']
First, we can see that encoding documents as graphs significantly outperforms the SeqPointer, which encodes documents with bi-directional RNN, especially when more GCN layers are used. Especially, we notice that even 1-layer GCN can outperform RNN encoder on some metrics. This shows that the graph-based representation are very suitable for the keyphrase extraction task and demonstrates the effectiveness of graph-based encoder in aggregating information of multiple appearances of identical words and modeling long-term dependency.