  Keyphrase generation aims to summarize long documents with a collection of
salient phrases. Deep neural models have demonstrated a remarkable success in
this task, capable of predicting keyphrases that are even absent from a
document. However, such abstractiveness is acquired at the expense of a
substantial amount of annotated data. In this paper, we present a novel method
for keyphrase generation, AutoKeyGen, without the supervision of any human
annotation. Motivated by the observation that an absent keyphrase in one
document can appear in other places, in whole or in part, we first construct a
phrase bank by pooling all phrases in a corpus. With this phrase bank, we then
draw candidate absent keyphrases for each document through a partial matching
process. To rank both types of candidates, we combine their lexical- and
semantic-level similarities to the input document. Moreover, we utilize these
top-ranked candidates as to train a deep generative model for more absent
keyphrases. Extensive experiments demonstrate that AutoKeyGen outperforms all
unsupervised baselines and can even beat strong supervised methods in certain
cases.
