  Keyword extraction is the process of identifying the words or phrases that
express the main concepts of text to the best of one's ability. Electronic
infrastructure creates a considerable amount of text every day and at all
times. This massive volume of documents makes it practically impossible for
human resources to study and manage them. Nevertheless, the need for these
documents to be accessed efficiently and effectively is evident in numerous
purposes. A blog, news article, or technical note is considered a relatively
long text since the reader aims to learn the subject based on keywords or
topics. Our approach consists of a combination of two models: graph centrality
features and textural features. The proposed method has been used to extract
the best keyword among the candidate keywords with an optimal combination of
graph centralities, such as degree, betweenness, eigenvector, closeness
centrality and etc, and textural, such as Casing, Term position, Term frequency
normalization, Term different sentence, Part Of Speech tagging. There have also
been attempts to distinguish keywords from candidate phrases and consider them
on separate keywords. For evaluating the proposed method, seven datasets were
used: Semeval2010, SemEval2017, Inspec, fao30, Thesis100, pak2018, and
Wikinews, with results reported as Precision, Recall, and F- measure. Our
proposed method performed much better in terms of evaluation metrics in all
reviewed datasets compared with available methods in literature. An approximate
16.9% increase was witnessed in F-score metric and this was much more for the
Inspec in English datasets and WikiNews in forgone languages.
