Exploring Clustering for Multi-document Arabic Summarisation

  • Mahmoud El-Haj
  • Udo Kruschwitz
  • Chris Fox
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7097)


In this paper we explore clustering for multi-document Arabic summarisation. For our evaluation we use an Arabic version of the DUC-2002 dataset that we previously generated using Google Translate. We explore how clustering (at the sentence level) can be applied to multi-document summarisation as well as for redundancy elimination within this process. We use different parameter settings including the cluster size and the selection model applied in the extractive summarisation process. The automatically generated summaries are evaluated using the ROUGE metric, as well as precision and recall. The results we achieve are compared with the top five systems in the DUC-2002 multi-document summarisation task.


Machine Translation Statistical Machine Translation Computational Linguistics Parallel Corpus Arabic Version 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mahmoud El-Haj
    • 1
  • Udo Kruschwitz
    • 1
  • Chris Fox
    • 1
  1. 1.Computer Science and Electronic EngineeringUniversity of EssexUnited Kingdom

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