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Exploring clustering for multi-document Arabic summarisation

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Publication date2011
Host publicationInformation Retrieval Technology: 7th Asia Information Retrieval Societies Conference, AIRS 2011, Dubai, United Arab Emirates, December 18-20, 2011. Proceedings
EditorsMohamed Vall Mohamed Salem, Khaled Shaalan, Farhad Oroumchian, Azadeh Shakery, Halim Khelalfa
Place of PublicationBerlin
PublisherSpringer
Pages550-561
Number of pages12
ISBN (electronic)9783642256318
ISBN (print)9783642256301
<mark>Original language</mark>English

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume7097
ISSN (Print)0302-9743
ISSN (electronic)1611-9743

Abstract

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.