An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
Table 2: MAP@10 scores of total keyphrase predictions. The best results are bold and the second best results are underlined. The meanings of the subscripts and the ‘*’ are the same as in Table 1.
['[BOLD] Model', '[BOLD] Inspec', '[BOLD] Krapivin', '[BOLD] NUS', '[BOLD] SemEval', '[BOLD] KP20k']
This table displays our KG-KE-KR-M method consistently outperforms the state-of-the-art models CopyRNN and CorrRNN demonstrating the effectiveness of our method. Moreover, we also observe that the KG-KE model exceeds CopyRNN and CorrRNN on most datasets, which indicates the strength of our combination with the extractive model. Besides, we also see the KG-KR model perform comparably or better than the baselines, suggesting the effective guidance ability of the retrieved keyphrases. In addition, after combining these two ideas, the KG-KE-KR model surpasses both or one of KG-KE and KG-KR on all datasets, which shows the effectiveness of the combination with extraction model and the retrieved keyphrases again. Finally, the performance gap between KG-KE-KR and KG-KE-KR-M implies the power of our merging module.