Automatic Indexing and Abstracting of Document Texts

1. Front Matter

   Pages i-xvi

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2. The Indexing and Abstracting Environment

   1. The Need for Indexing and Abstracting Texts

      Pages 3-26

   2. The Attributes of Text

      Pages 27-48

   3. Text Representations and Their Use

      Pages 49-73

3. Methods of Automatic Indexing and Abstracting

   1. Automatic Indexing: The Selection of Natural Language Index Terms

      Pages 77-102

   2. Automatic Indexing: The Assignment of Controlled Language Index Terms

      Pages 103-132

   3. Automatic Abstracting: The Creation of Text Summaries

      Pages 133-154

4. Applications

   1. Text Structuring and Categorization When Summarizing Legal Cases

      Pages 157-172

   2. Clustering of Paragraphs When Summarizing Legal Cases

      Pages 173-190

   3. The Creation of Highlight Abstracts of Magazine Articles

      Pages 191-205

   4. The Assignment of Subject Descriptors to Magazine Articles

      Pages 207-226

5. Back Matter

   Pages 227-265

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Automatic Indexing and Abstracting of Document Texts summarizes the latest techniques of automatic indexing and abstracting, and the results of their application. It also places the techniques in the context of the study of text, manual indexing and abstracting, and the use of the indexing descriptions and abstracts in systems that select documents or information from large collections. Important sections of the book consider the development of new techniques for indexing and abstracting. The techniques involve the following: using text grammars, learning of the themes of the texts including the identification of representative sentences or paragraphs by means of adequate cluster algorithms, and learning of classification patterns of texts. In addition, the book is an attempt to illuminate new avenues for future research.
Automatic Indexing and Abstracting of Document Texts is an excellent reference for researchers and professionals working in the field of content management and information retrieval.

• algorithms
• classification
• clustering
• control
• grammar
• information
• information retrieval
• knowledge
• learning
• natural language

• Katholieke Universiteit Leuven, Belgium

  Marie-Francine Moens

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