Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction
Table 2: Comparison results on INSPEC dataset
['[BOLD] Method', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score']
The first three systems are the ones presented in [Hulth:2003], with three different candidate keyphrase generation techniques: n-grams, Noun Phrase (NP) chunking, and patterns. The fourth system is TopicRank [bougouin2013], a graph-based keyphrase extraction method that relies on a topical representation of the document. Our proposed solution achieves best performance in term of F1-score and Recall. Although TopicRank obtains best performance in precision, its recall results are significantly worse than the ones obtained by us; moreover, we have to stress that we’re able to obtain better precision when using an embedding size of 200 and 300, albeit with a slightly lower overall F1-Score. Finally, it’s worth noting that we perform better than the results presented in [meng2017deep], which is to the best of our knowledge the only one DL AKE algorithm evaluated on the INSPEC dataset. In fact, we obtain a F1@10 score of 0.422, while the best F1@10 score obtained by [meng2017deep] is 0.342.