Keyphrase Generation for Scientific Articles using GANs
Table 1: Extractive and Abstractive Keyphrase Metrics
['Model', 'Score', 'Inspec', 'Krapivin', 'NUS', 'KP20k']
We compare our proposed approach against 2 baseline models - catSeq (Yuan et al. 2018), RL-based catSeq Model (Chan et al. 2019) in terms of F1 scores as explained in (Yuan et al. 2018). The results, summarized in Table 1, are broken down in terms of performance on extractive and abstractive keyphrases. For extractive keyphrases, our proposed model performs better than the pre-trained catSeq model on all datasets but is slightly worse than catSeq-RL except for on Krapivin where it obtains the best F1@M of 0.37. On the other hand, for abstractive keyphrases, our model performs better than the other two baselines on three of four datasets suggesting that GAN models are more effective in generation of keyphrases.