  Keyphrase generation (KG) aims to summarize the main ideas of a document into
a set of keyphrases. A new setting is recently introduced into this problem, in
which, given a document, the model needs to predict a set of keyphrases and
simultaneously determine the appropriate number of keyphrases to produce.
Previous work in this setting employs a sequential decoding process to generate
keyphrases. However, such a decoding method ignores the intrinsic hierarchical
compositionality existing in the keyphrase set of a document. Moreover,
previous work tends to generate duplicated keyphrases, which wastes time and
computing resources. To overcome these limitations, we propose an exclusive
hierarchical decoding framework that includes a hierarchical decoding process
and either a soft or a hard exclusion mechanism. The hierarchical decoding
process is to explicitly model the hierarchical compositionality of a keyphrase
set. Both the soft and the hard exclusion mechanisms keep track of
previously-predicted keyphrases within a window size to enhance the diversity
of the generated keyphrases. Extensive experiments on multiple KG benchmark
datasets demonstrate the effectiveness of our method to generate less
duplicated and more accurate keyphrases.
