The supervised approaches treat keyphrase extraction as a binary classification task, in which a learning model is trained on the features of labeled keyphrases to determine whether a candidate phrase is a keyphrase (Witten et al., 1999;Medelyan et al., 2009;.
the unsupervised approaches directly treat keyphrase extraction as a ranking problem, scoring each candidate using different kinds of techniques such as clustering (Liu et al., 2009), or graph-based ranking (Mihalcea and Tarau, 2004;Wan and Xiao, 2008).