Collaborative Ranking between Supervised and Unsupervised Approaches for Keyphrase Extraction

Automatic keyphrase extraction methods have generally taken either supervised or unsupervised approaches. Supervised methods extract keyphrases by using a training document set, thus acquiring knowledge from a global collection of texts. Conversely, unsupervised methods extract keyphrases by determining their relevance in a single-document context, without prior learning. We present a hybrid keyphrase extraction method for short articles, HybridRank, which leverages the benefits of both approaches. Our system implements modified versions of the TextRank (Mihalcea and Tarau, 2004)—unsupervised—and KEA (Witten et al., 1999)—supervised—methods, and applies a merging algorithm to produce an overall list of keyphrases. We have tested HybridRank on more than 900 abstracts belonging to a wide variety of subjects, and show its superior effectiveness. We conclude that knowledge collaboration between supervised and unsupervised methods can produce higher-quality keyphrases than applying these methods individually.