Automatic Text Summarization
- 4h 52m
- Juan-Manuel Torres-Moreno (ed)
- John Wiley & Sons (US)
- 2014
Textual information in the form of digital documents quickly accumulates to create huge amounts of data. The majority of these documents are unstructured: it is unrestricted text and has not been organized into traditional databases. Processing documents is therefore a perfunctory task, mostly due to a lack of standards. It has thus become extremely difficult to implement automatic text analysis tasks. Automatic Text Summarization (ATS), by condensing the text while maintaining relevant information, can help to process this ever-increasing, difficult-to-handle, mass of information.
This book examines the motivations and different algorithms for ATS. The author presents the recent state of the art before describing the main problems of ATS, as well as the difficulties and solutions provided by the community. The book provides recent advances in ATS, as well as current applications and trends. The approaches are statistical, linguistic and symbolic. Several examples are also included in order to clarify the theoretical concepts.
About the Editor
PhD (INPG-Grenoble), HDR and Maître de Conférences in Computer Science at UAPV (Avignon), Juan-Manuel Torres is asociated profesor at Ecole Polytechnique (Montréal, Canada). He works on algorithms of automatic text summarization since 2001.
Books:
- Automatic Text Summarization - Wiley & Sons 2014
- Résumé Automatique de Documents: une approche statsistique - Hermès 2011
In this Book
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Foreword by A. Zamora and R. Salvador
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Foreword by H. Saggion
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Notation
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Introduction
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Why Summarize Texts?
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Automatic Text Summarization—Some Important Concepts
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Single-Document Summarization
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Guided Multi-Document Summarization
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Multi and Cross-Lingual Summarization
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Source and Domain-Specific Summarization
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Text Abstracting
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Evaluating Document Summaries
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Conclusion
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Bibliography
