Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels
Table 2: Automatic metrics and human evaluations.
['[BOLD] Model', '[BOLD] BLEU', '[BOLD] ROUGE-L', '[BOLD] Relevance', '[BOLD] Fluency', '[BOLD] Informativeness']
We also provide one user comment for each passage to get evaluations of human performance. All these passage-comment pairs are labeled by human annotators. In relevance metric, our full model EKG+GAT(V+E) has better relevance score than all the baselines. It means our model can generate more relevant comments and better reflect the main storyline of the novel. For all this, there still exists significant gaps when compared to the human performance. In fluency and informativeness metrics, our EKG+GAT(V+E) model has achieves higher score compared to all baselines. It illustrates that the generated comments by our proposed model are more fluent and contains more attractive information.