Identifying Machine-Paraphrased Plagiarism

README.txt

Title: Identifying Machine-Paraphrased Plagiarism
Authors: Jan Philip Wahle, Terry Ruas, Tomas Foltynek, Norman Meuschke, and  Bela Gipp
contact email: wahle@gipplab.org; ruas@gipplab.org;
Venue: iConference
Year: 2022
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Dataset Description:

Training:
200,767 paragraphs (98,282 original, 102,485paraphrased) extracted from 8,024 Wikipedia (English) articles (4,012 original, 4,012 paraphrased using the SpinBot API).

Testing:
SpinBot: 
    arXiv         – Original – 20,966;    Spun – 20,867
    Theses        – Original – 5,226;        Spun – 3,463
    Wikipedia    – Original – 39,241;    Spun – 40,729
    
SpinnerChief-4W: 
    arXiv         – Original – 20,966;    Spun – 21,671
    Theses        – Original – 2,379;        Spun – 2,941
    Wikipedia    – Original – 39,241;    Spun – 39,618
    
SpinnerChief-2W: 
    arXiv         – Original – 20,966;    Spun – 21,719
    Theses        – Original – 2,379;        Spun – 2,941
    Wikipedia    – Original – 39,241;    Spun – 39,697

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Dataset Structure:

[human_evaluation] folder: human evaluation to identify human-generated text and machine-paraphrased text. It contains the files (original and spun) as for the answer-key for the survey performed with human subjects (all data is anonymous for privacy reasons).

NNNNN.txt – whole document from which an extract was taken for human evaluation
    key.txt.zip – information about each case (ORIG/SPUN)
    results.xlsx – raw results downloaded from the survey tool (the extracts which humans judged are in the first line)
    results-corrected.xlsx – at the very beginning, there was a mistake in one question (wrong extract). These results were excluded.

[automated_evaluation]: contains all files used for the automated evaluation considering [spinbot] (https://spinbot.com/API) and [spinnerchief] (http://developer.spinnerchief.com/API_Document.aspx).

• Each paraphrase tool folder contains:
• [corpus]
  

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