A flexible keyphrase extraction technique for academic literature - Gollam, Rabby; Azad, Saiful; Paolo, Casari; Kamal Z., Zamli; Mohammed Mostafizur, Rahman (Elsevier B.V., 2018)
A keyphrase extraction technique endeavors to extract quality keyphrases from a given document, which provide a high-level summary of that document. Except statistical keyphrase extraction approaches, all other approaches are either domain-dependent or require a sucient amount of training data, which are rare at present. Therefore, in this paper, a new tree-based automatic keyphrase extraction technique is proposed, which is domain-independent and employs nominal statistical knowledge; but no train data are required. The proposed technique extracts a quality keyphrase through forming a tree from a candidate keyphrase; and later, it is expanded or shrunk or remained in the same state depending on other similar candidate keyphrases. At the end, keyphrases are extracted from the resultant trees based on a value, (which is the Maturity Index (MI) of a node in the tree), which enables flexibility in this process. A small value would yield many and/or lengthy keyphrases (greedy approach); whereas, a large value would yield lower and/or abbreviated keyphrases (conservative approach). Thereby, a user can extract his/her desired-level of keyphrases through tuning value. The eectiveness of the proposed technique is evaluated on an actual corpus, and compared with Rapid Automatic Keyphrase Extraction (RAKE) technique.