| Summary: | Automatic keyphrase extraction techniques aim to extract quality keyphrases for higher level summarization of a document.
Majority of the existing techniques are mainly domain-specific, which require application domain knowledge and employ
higher order statistical methods, and computationally expensive and require large train data, which is rare for many
applications. Overcoming these issues, this paper proposes a new unsupervised keyphrase extraction technique. The
proposed unsupervised keyphrase extraction technique, named TeKET or Tree-based Keyphrase Extraction Technique, is
a domain-independent technique that employs limited statistical knowledge and requires no train data. This technique
also introduces a new variant of a binary tree, called KeyPhrase Extraction (KePhEx) tree, to extract final keyphrases
from candidate keyphrases. In addition, a measure, called Cohesiveness Index or CI, is derived which denotes a given
node’s degree of cohesiveness with respect to the root. The CI is used in flexibly extracting final keyphrases from the
KePhEx tree and is co-utilized in the ranking process. The effectiveness of the proposed technique and its domain and
language independence are experimentally evaluated using available benchmark corpora, namely SemEval-2010 (a scientific
articles dataset), Theses100 (a thesis dataset), and a German Research Article dataset, respectively. The acquired results
are compared with other relevant unsupervised techniques belonging to both statistical and graph-based techniques. The
obtained results demonstrate the improved performance of the proposed technique over other compared techniques in terms
of precision, recall, and F1 scores.
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