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In this paper, we present a supervised framework for automatic keywordextraction from single document. We model the text as complex network, andconstruct the feature set by extracting select node properties from it. Severalnode properties have been exploited by unsupervised, graph-based keywordextraction methods to discriminate keywords from non-keywords. We exploit thecomplex interplay of node properties to design a supervised keyword extractionmethod. The training set is created from the feature set by assigning a labelto each candidate keyword depending on whether the candidate is listed as agold-standard keyword or not. Since the number of keywords in a document ismuch less than non-keywords, the curated training set is naturally imbalanced.We train a binary classifier to predict keywords after balancing the trainingset. The model is trained using two public datasets from scientific domain andtested using three unseen scientific corpora and one news corpus. Comparativestudy of the results with several recent keyword and keyphrase extractionmethods establishes that the proposed method performs better in most cases.This substantiates our claim that graph-theoretic properties of words areeffective discriminators between keywords and non-keywords. We support ourargument by showing that the improved performance of the proposed method isstatistically significant for all datasets. We also evaluate the effectivenessof the pre-trained model on Hindi and Assamese language documents. We observethat the model performs equally well for the cross-language text even though itwas trained only on English language documents. This shows that the proposedmethod is independent of the domain, collection, and language of the trainingcorpora.

Complex Network based Supervised Keyword Extractor