1. Some discussions are required on the convergence of the proposed joint learning process (for RNN and CopyRNN), so that readers can understand, how the stable points in probabilistic metric space are obtained? Otherwise, it may be tough to repeat the results.
2. The evaluation process shows that the current system (which extracts 1.
Present and 2. Absent both kinds of keyphrases) is evaluated against baselines (which contains only "present" type of keyphrases). Here there is no direct comparison of the performance of the current system w.r.t. other state-of-the-arts/benchmark systems on only "present" type of key phrases. It is important to note that local phrases (keyphrases) are also important for the document. The experiment does not discuss it explicitly. It will be interesting to see the impact of the RNN and Copy RNN based model on automatic extraction of local or "present" type of key phrases.
3. The impact of document size in keyphrase extraction is also an important point. It is found that the published results of [1], (see reference below) performs better than (with a sufficiently high difference) the current system on Inspec (Hulth, 2003) abstracts dataset. 4. It is reported that current system uses 527,830 documents for training, while 40,000 publications are held out for training baselines. Why are all publications not used in training the baselines? Additionally, The topical details of the dataset (527,830 scientific documents) used in training RNN and Copy RNN are also missing. This may affect the chances of repeating results.
5. As the current system captures the semantics through RNN based models. So, it would be better to compare this system, which also captures semantics. Even, Ref-[2] can be a strong baseline to compare the performance of the current system.
Suggestions to improve: 1. As, per the example, given in the Figure-1, it seems that all the "absent" type of key phrases are actually "Topical phrases". For example: "video search", "video retrieval", "video indexing" and "relevance ranking", etc.
These all define the domain/sub-domain/topics of the document. So, In this case, it will be interesting to see the results (or will be helpful in evaluating "absent type" keyphrases): if we identify all the topical phrases of the entire corpus by using tf-idf and relate the document to the high-ranked extracted topical phrases (by using Normalized Google Distance, PMI, etc.). As similar efforts are already applied in several query expansion techniques (with the aim to relate the document with the query, if matching terms are absent in document).
Reference: 1. Liu, Zhiyuan, Peng Li, Yabin Zheng, and Maosong Sun. 2009b. Clustering to find exemplar terms for keyphrase extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pages 257–266.
2. Zhang, Q., Wang, Y., Gong, Y., & Huang, X. (2016). Keyphrase extraction using deep recurrent neural networks on Twitter. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (pp. 836-845).
1. Some discussions are required on the convergence of the proposed joint learning process (for RNN and CopyRNN), so that readers can understand, how the stable points in probabilistic metric space are obtained? Otherwise, it may be tough to repeat the results.