An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
Table 3: Ablation study of the candidate sources of Algorithm 1 on Krapivin dataset. “no merging” means we do not use the merging algorithm.
['Candidate Sources', 'Total F1@10', 'Present F1@5', 'Absent R@10']
When comparing “gk” with “gk, no merging”, we can see that the merging algorithm improves the performance of total and present keyphrase predictions, but it degrades the performance of absent keyphrase prediction. These results indicate the trained scorer performs better on scoring present keyphrases than scoring absent keyphrases. One possible reason is that scoring absent keyphrases requires a stronger text understanding ability than scoring present keyphrases. Besides absent keyphrase prediction, it is observed that the retrieved keyphrases can also benefit the present keyphrase prediction. For the extracted keyphrases, as shown in the “gk,ek” row, they only improve the present keyphrase prediction ability and do not affect absent keyphrases as we anticipated.