Abstract: Background: Keyword extraction is a popular research topic in the field of
natural language processing. Keywords are terms that describe the most relevant
information in a document. The main problem that researchers are facing is how
to efficiently and accurately extract the core keywords from a document.
However, previous keyword extraction approaches have utilized the text and
graph features, there is the lack of models that can properly learn and combine
these features in a best way.
Methods: In this paper, we develop a multimodal Key-phrase extraction
approach, namely Phraseformer, using transformer and graph embedding
techniques. In Phraseformer, each keyword candidate is presented by a vector
which is the concatenation of the text and structure learning representations.
Phraseformer takes the advantages of recent researches such as BERT and ExEm to
preserve both representations. Also, the Phraseformer treats the key-phrase
extraction task as a sequence labeling problem solved using classification
Results: We analyze the performance of Phraseformer on three datasets
including Inspec, SemEval2010 and SemEval 2017 by F1-score. Also, we
investigate the performance of different classifiers on Phraseformer method
over Inspec dataset. Experimental results demonstrate the effectiveness of
Phraseformer method over the three datasets used. Additionally, the Random
Forest classifier gain the highest F1-score among all classifiers.
Conclusions: Due to the fact that the combination of BERT and ExEm is more
meaningful and can better represent the semantic of words. Hence, Phraseformer
significantly outperforms single-modality methods.