Title-Guided Encoding for Keyphrase Generation
Table 4: Absent keyphrase predicting results on all test datasets. “% gain” is the improvement gain over CopyCNN.
['[BOLD] Model', '[BOLD] Inspec R@10', '[BOLD] Inspec R@50', '[BOLD] Krapivin R@10', '[BOLD] Krapivin R@50', '[BOLD] NUS R@10', '[BOLD] NUS R@50', '[BOLD] SemEval R@10', '[BOLD] SemEval R@50', '[BOLD] KP20k R@10', '[BOLD] KP20k R@50']
It is observed that our TG-Net model consistently outperforms the previous sequence-to-sequence models on all the datasets. For instance, our model exceeds 19.1% (R@50 score) on KP20k than the state-of-the-art model CopyCNN. Overall, the results indicate that our model is able to capture the underlying semantic meaning of the context content much better than these baselines, as we have anticipated.