  Neural keyphrase generation models have recently attracted much interest due
to their ability to output absent keyphrases, that is, keyphrases that do not
appear in the source text. In this paper, we discuss the usefulness of absent
keyphrases from an Information Retrieval (IR) perspective, and show that the
commonly drawn distinction between present and absent keyphrases is not made
explicit enough. We introduce a finer-grained categorization scheme that sheds
more light on the impact of absent keyphrases on scientific document retrieval.
Under this scheme, we find that only a fraction (around 20%) of the words that
make up keyphrases actually serves as document expansion, but that this small
fraction of words is behind much of the gains observed in retrieval
effectiveness. We also discuss how the proposed scheme can offer a new angle to
evaluate the output of neural keyphrase generation models.
