BUAP: An Unsupervised Approach to Automatic Keyphrase Extraction from Scientific Articles

In this paper, it is presented an unsupervised approach to automatically discover the latent keyphrases contained in scientific articles. The proposed technique is constructed on the basis of the combination of two techniques: maximal frequent sequences and pageranking. We evaluated the obtained results by using micro-averaged precision, recall and F-scores with respect to two different gold standards: 1) reader's keyphrases, and 2) a combined set of author's and reader's keyphrases. The obtained results were also compared against three different baselines: one unsupervised (TF-IDF based) and two supervised (Naive Bayes and Maximum Entropy).