  Keyphrases are capable of providing semantic metadata characterizing
documents and producing an overview of the content of a document. Since
keyphrase extraction is able to facilitate the management, categorization, and
retrieval of information, it has received much attention in recent years. There
are three approaches to address keyphrase extraction: (i) traditional two-step
ranking method, (ii) sequence labeling and (iii) generation using neural
networks. Two-step ranking approach is based on feature engineering, which is
labor intensive and domain dependent. Sequence labeling is not able to tackle
overlapping phrases. Generation methods (i.e., Sequence-to-sequence neural
network models) overcome those shortcomings, so they have been widely studied
and gain state-of-the-art performance. However, generation methods can not
utilize context information effectively. In this paper, we propose a novelty
Span Keyphrase Extraction model that extracts span-based feature representation
of keyphrase directly from all the content tokens. In this way, our model
obtains representation for each keyphrase and further learns to capture the
interaction between keyphrases in one document to get better ranking results.
In addition, with the help of tokens, our model is able to extract overlapped
keyphrases. Experimental results on the benchmark datasets show that our
proposed model outperforms the existing methods by a large margin.
