Keyphrase extraction using semantic networks structure analysis

Keyphrases play a key role in text indexing, summarization and categorization. However, most of the existing keyphrase extraction approaches require human-labeled training sets. In this paper, we propose an automatic keyphrase extraction algorithm, which can be used in both supervised and unsupervised tasks. This algorithm treats each document as a semantic network. Structural dynamics of the network, are used to extract keyphrases (key nodes) unsupervised Experiments demonstrate the proposed algorithm averagely improves 50% in effectiveness and 30% in efficiency. in unsupervised tasks and performs comparatively with supervised extractors. Moreover, by applying this algorithm to supervised tasks, we develop a classifier with an overall accuracy up to 80%