  Keyword and keyphrase extraction is an important problem in natural language
processing, with applications ranging from summarization to semantic search to
document clustering. Graph-based approaches to keyword and keyphrase extraction
avoid the problem of acquiring a large in-domain training corpus by applying
variants of PageRank algorithm on a network of words. Although graph-based
approaches are knowledge-lean and easily adoptable in online systems, it
remains largely open whether they can benefit from centrality measures other
than PageRank. In this paper, we experiment with an array of centrality
measures on word and noun phrase collocation networks, and analyze their
performance on four benchmark datasets. Not only are there centrality measures
that perform as well as or better than PageRank, but they are much simpler
(e.g., degree, strength, and neighborhood size). Furthermore, centrality-based
methods give results that are competitive with and, in some cases, better than
two strong unsupervised baselines.
