  Keyphrase extraction is a fundamental task in natural language processing and
information retrieval that aims to extract a set of phrases with important
information from a source document. Identifying important keyphrase is the
central component of the keyphrase extraction task, and its main challenge is
how to represent information comprehensively and discriminate importance
accurately. In this paper, to address these issues, we design a new hyperbolic
matching model (HyperMatch) to represent phrases and documents in the same
hyperbolic space and explicitly estimate the phrase-document relevance via the
Poincar\'e distance as the important score of each phrase. Specifically, to
capture the hierarchical syntactic and semantic structure information,
HyperMatch takes advantage of the hidden representations in multiple layers of
RoBERTa and integrates them as the word embeddings via an adaptive mixing
layer. Meanwhile, considering the hierarchical structure hidden in the
document, HyperMatch embeds both phrases and documents in the same hyperbolic
space via a hyperbolic phrase encoder and a hyperbolic document encoder. This
strategy can further enhance the estimation of phrase-document relevance due to
the good properties of hyperbolic space. In this setting, the keyphrase
extraction can be taken as a matching problem and effectively implemented by
minimizing a hyperbolic margin-based triplet loss. Extensive experiments are
conducted on six benchmarks and demonstrate that HyperMatch outperforms the
state-of-the-art baselines.
