Plagiarism Detection using ROUGE and WordNet

TL;DR

This paper proposes a novel plagiarism detection method combining ROUGE metrics and WordNet to improve detection of modified and paraphrased content, addressing limitations of traditional fingerprinting approaches.

Contribution

It introduces a hybrid approach using ROUGE and WordNet, enhancing detection of various types of text modifications in plagiarism cases.

Findings

ROUGE-based metrics detect verbatim and sentence modifications

WordNet improves detection of word substitutions

The combined method handles simple text additions or deletions effectively

Abstract

With the arrival of digital era and Internet, the lack of information control provides an incentive for people to freely use any content available to them. Plagiarism occurs when users fail to credit the original owner for the content referred to, and such behavior leads to violation of intellectual property. Two main approaches to plagiarism detection are fingerprinting and term occurrence; however, one common weakness shared by both approaches, especially fingerprinting, is the incapability to detect modified text plagiarism. This study proposes adoption of ROUGE and WordNet to plagiarism detection. The former includes ngram co-occurrence statistics, skip-bigram, and longest common subsequence (LCS), while the latter acts as a thesaurus and provides semantic information. N-gram co-occurrence statistics can detect verbatim copy and certain sentence modification, skip-bigram and LCS are immune from text modification such as simple addition or deletion of words, and WordNet may handle the problem of word substitution.