  Keyword extraction is called identifying words or phrases that express the
main concepts of texts in best. There is a huge amount of texts that are
created every day and at all times through electronic infrastructure. So, it is
practically impossible for humans to study and manage this volume of documents.
However, the need for efficient and effective access to these documents is
evident in various purposes. Weblogs, News, and technical notes are almost long
texts, while the reader seeks to understand the concepts by topics or keywords
to decide for reading the full text. To this aim, we use a combined approach
that consists of two models of graph centrality features and textural features.
In the following, graph centralities, such as degree, betweenness, eigenvector,
and closeness centrality, have been used to optimally combine them to extract
the best keyword among the candidate keywords extracted by the proposed method.
Also, another approach has been introduced to distinguishing keywords among
candidate phrases and considering them as a separate keyword. To evaluate the
proposed method, seven datasets named, Semeval2010, SemEval2017, Inspec, fao30,
Thesis100, pak2018 and WikiNews have been used, and results reported Precision,
Recall, and F- measure.
