Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Introduction to the special issue on summarization
Computational Linguistics - Summarization
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Automatic summarising: The state of the art
Information Processing and Management: an International Journal
Proceedings of the forty-first annual ACM symposium on Theory of computing
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
Kronecker Graphs: An Approach to Modeling Networks
The Journal of Machine Learning Research
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks: An Introduction
On helmholtz's principle for documents processing
Proceedings of the 10th ACM symposium on Document engineering
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Automatic text summarization is an important and challenging problem. Over the years, the amount of text available electronically has grown exponentially. This growth has created a huge demand for automatic methods and tools for text summarization. We can think of automatic summarization as a type of information compression. To achieve such compression, better modelling and understanding of document structures and internal relations is required. In this article, we develop a novel approach to extractive text summarization by modelling texts and documents as small-world networks. Based on our recent work on the detection of unusual behavior in text, we model a document as a one-parameter family of graphs with its sentences or paragraphs defining the vertex set and with edges defined by Helmholtz's principle. We demonstrate that for some range of the parameters, the resulting graph becomes a small-world network. Such a remarkable structure opens the possibility of applying many measures and tools from social network theory to the problem of extracting the most important sentences and structures from text documents. We hope that documents will be also a new and rich source of examples of complex networks.