Citation Sentence Reuse Behavior of Scientists: A Case Study on Massive Bibliographic Text Dataset of Computer Science

Abstract

Our current knowledge of scholarly plagiarism is largely based on the similarity between full text research articles. In this paper, we propose an innovative and novel conceptualization of scholarly plagiarism in the form of reuse of explicit citation sentences in scienti c research articles. Note that while full-text plagiarism is an indicator of a gross-level behavior, copying of citation sentences is a more nuanced micro-scale phenomenon observed even for well-known researchers.e current work poses several interesting questions and a empts to answer them by empirically investigating a large bibliographic text dataset from computer science containing millions of lines of citation sentences. In particular, we report evidences of massive copying behavior. We also present several striking real examples throughout the paper to showcase widespread adoption of this undesirable practice. In contrast to the popular perception, we nd that copying tendency increases as an author matures. e copying behavior is reported to exist in all elds of computer science; however, the theoretical elds indicate more copying than the applied elds. CCS CONCEPTS•Information systems →Near-duplicate and plagiarism detection; KEYWORDSCitation context, plagiarism, text reuse ACM Reference format: Mayank Singh, Abhishek Niranjan, Divyansh Gupta, Nikhil Angad Bakshi, Animesh Mukherjee, and Pawan Goyal. 2017. Citation sentence reuse behavior of scientists: A case study on massive bibliographic text dataset of computer science.

Characterizing Human Summarization Strategies for Text Reuse and Transformation in Literature Review Writing

“…A major contribution of our work lies in bringing the insights of text reuse and citation analysis approaches to a primarily data-driven automatic summarization community. Studies of the text reuse in citances have used automatic text matching on large scientific corpora, such as the arXiv database (Citron and Ginsparg 2015) and the Microsoft Academic Search dataset (Singh et al 2017). They have been supplemented with an empirical investigation of research fields, author influence, countries of origin, and article impact to obtain insights into the prevalent practices in different communities.…”