  Term weighting schemes are widely used in Natural Language Processing and
Information Retrieval. In particular, term weighting is the basis for keyword
extraction. However, there are relatively few evaluation studies that shed
light about the strengths and shortcomings of each weighting scheme. In fact,
in most cases researchers and practitioners resort to the well-known tf-idf as
default, despite the existence of other suitable alternatives, including
graph-based models. In this paper, we perform an exhaustive and large-scale
empirical comparison of both statistical and graph-based term weighting methods
in the context of keyword extraction. Our analysis reveals some interesting
findings such as the advantages of the less-known lexical specificity with
respect to tf-idf, or the qualitative differences between statistical and
graph-based methods. Finally, based on our findings we discuss and devise some
suggestions for practitioners. Source code to reproduce our experimental
results, including a keyword extraction library, are available in the following
repository: https://github.com/asahi417/kex
