• Task: Given a set of suspicious documents and a set of potential source documents, the task is to find all plagiarized passages in the suspicious documents and their corresponding source passages in the source documents.
  • Input: [data]
  • Evaluator: [code]


Given a set of suspicious documents and a set of source documents, the task is to find all plagiarized sections in the suspicious documents and, if available, the corresponding source sections.

Remark. This task combines both external plagiarism detection and intrinsic plagiarism detection, where the former refers to detecting plagiarized sections in a suspicious document and the corresponding source sections in a given set of source documents, and the latter refers to detecting plagiarized sections without comparing the suspicious document to any other documents, e.g., by detecting changes in writing style.


We are happy to announce the following overall winner of the 2st International Competition on Plagiarism Detection who will be awarded 500,- Euro sponsored by Yahoo! Research:

  • J. Kasprzak and M. Brandejs from Masaryk University, Czech Republic



To develop your approach, we provide you with a training corpus which comprises a set of suspicious documents and a set of source documents. A suspicious document may contain plagiarized passages, the source passages of which may or may not be present in one or more of the source documents. Learn more »


For each suspicious document suspicious-documentXYZ.txt found in the evaluation corpora, your plagiarism detector shall output an XML file suspicious-documentXYZ.xml which contains meta information about all plagiarism cases detected within:

<document reference="suspicious-documentXYZ.txt">

The source_* attributes may be omitted in case no source document can be identified for a given detected plagiarized passage.


Performance will be measured using macro-averaged precision and recall, granularity, and the plagdet score, which is a combination of the first three measures. For your convenience, we provide a reference implementation of the measures written in Python.


The following table lists the performances achieved by the participating teams:

Plagiarism Detection Performance
Plagdet Participant
0.7971 J. Kasprzak and M. Brandejs
Masaryk University, Czech Republic
0.7090 D. Zou, W. Long, and Z. Ling
South China University of Technology, China
0.6948 M. Muhr, R. Kern, M. Zechner, and M. Granitzer
Know-Center Graz, Austria
0.6209 C. Grozea* and M. Popescu°
*Fraunhofer FIRST, Germany
°University of Bucharest, Romania
0.6066 G. Oberreuter, G. L'Huillier, S.A. Ríos, and J.D. Velásquez
University of Chile, Chile
0.5851 D.A.R. Torrejón*,° and J.M.M. Ramos°
*IES "José Caballero", Spain
°Universidad de Huelva, Spain
0.5191 R.C. Pereira, V.P. Moreira, and R. Galante
Universidade Federal do Rio Grande do Sul, Brazil
0.5093 Y. Palkovskii, A. Belov, and I. Muzika
Zhytomyr State University and SkyLine, Inc. Ukraine
0.4378 Sobha L., Pattabhi R.K R., Vijay S.R., A. Akilandeswari
MIT Campus of Anna University Chennai, India
0.2564 T. Gottron
Universität Koblenz-Landau, Germany
0.2222 D. Micol, Ó. Ferrández, and R. Muñoz
University of Alicante, Spain
0.2148 M.R. Costa-jussà, R.E. Banchs, J. Grivolla, and J. Codina
Barcelona Media Research Center, Spain
0.2053 R.M.A. Nawab, M. Stevenson, and P. Clough
University of Sheffield, UK
0.2034 P. Gupta and S. Rao
DA-IICT, India
0.1375 C. Vania and M. Adriani
Universitas Indonesia, Indonesia
0.0558 P. Suárez*, J.C. González*,°, and J. Villena-Román*,^
*Daedalus - Data, Decisions and Language, Spain
°Universidad Politécnica de Madrid, Spain
^Universidad Carlos III de Madrid, Spain
0.0195 S. Alzahrani* and N. Salim°
*Taif University, Saudi Arabia
°Universiti Teknologi Malaysia, Malaysia
0.0008 A. Iftene et al.
University of Iasi, Romania

A more detailed analysis of the detection performances can be found in the overview paper accompanying this task.

Task Committee