A complex network approach to text summarization
Information Sciences: an International Journal
Web site topic-hierarchy generation based on link structure
Journal of the American Society for Information Science and Technology
Keyphrase extraction for labeling a website topic hierarchy
Proceedings of the 11th International Conference on Electronic Commerce
Exploiting semantic hierarchies for Flickr group
AMT'10 Proceedings of the 6th international conference on Active media technology
AZOM: a Persian structured text summarizer
NLDB'11 Proceedings of the 16th international conference on Natural language processing and information systems
Applied Computational Intelligence and Soft Computing
MCMR: Maximum coverage and minimum redundant text summarization model
Expert Systems with Applications: An International Journal
CDDS: Constraint-driven document summarization models
Expert Systems with Applications: An International Journal
Multiple documents summarization based on evolutionary optimization algorithm
Expert Systems with Applications: An International Journal
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Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractals—mathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents. © 2008 Wiley Periodicals, Inc.