Heterogeneous Graph Neural Networks for Extractive Document Summarization
Table 3: Ablation studies on CNN/DailyMail test set. We remove various modules and explore their influence on our model. ’-’ means we remove the module from the original HeterSUMGraph. Note that HeterSUMGraph without the updating of sentence nodes is actually the Ext-BiLSTM model described in Section 4.3.
['[BOLD] Model', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L']
We suspect that filtering noisy words enable the model to better focus on useful word nodes, at the cost of losing some bigram information. The residual connection plays an important role in the combination of the original representation and the updating message from another type of nodes, which cannot be replaced by the concatenation. Besides, the introduction of edge features, word update and BiLSTM initialization for sentences also show their effectiveness.