  Keyphrases efficiently summarize a document's content and are used in various
document processing and retrieval tasks. Several unsupervised techniques and
classifiers exist for extracting keyphrases from text documents. Most of these
methods operate at a phrase-level and rely on part-of-speech (POS) filters for
candidate phrase generation. In addition, they do not directly handle
keyphrases of varying lengths. We overcome these modeling shortcomings by
addressing keyphrase extraction as a sequential labeling task in this paper. We
explore a basic set of features commonly used in NLP tasks as well as
predictions from various unsupervised methods to train our taggers. In addition
to a more natural modeling for the keyphrase extraction problem, we show that
tagging models yield significant performance benefits over existing
state-of-the-art extraction methods.
