Comparing and Combining Content- and Citation-based Approaches for Plagiarism Detection

Abstract

The vast amount of scienti c publications available online makes it easier for students and researchers reusing text from other authors and makes it harder for checking the originality of a given text. Reusing text without crediting the original authors is considered plagiarism. A number of studies report on the high prevalence of plagiarism in academia. As a consequence, numerous institutions and researchers are dedicated to devising systems to automate the process of checking for plagiarism. This work focuses on the problem of detecting text reuse in scienti c papers. In this context, the contributions of this paper are twofold: (i) we survey the existing approaches for plagiarism detection based on content, based on content and structure, and based on citations and references; and (ii) we compare Content and Citation-based approaches with the goal of evaluating whether they are complementary and if their combination can improve the quality of the detection. We carried out experiments with real datasets of scienti c papers and concluded that a combination of the methods can be bene ficial.