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.

This third edition of what has become a classic among textbooks in schools of library and information science (and related programs) has been thoroughly updated to reflect the evolving technological advancements in the field. Focusing on indexing of the subject matter of material, the beginning chapters review the literature and discuss various principles and practices such as exhaustivity or depth of indexing, specificity, checktags, pre- and post-coordinate indexes, and consistency and quality of indexing. Discussions on abstracting cover such concepts as the different types of abstracts, purpose of an abstract, structured versus narrative abstracts, informative versus indicative abstracts, subject slanting, modular abstracts, and writing and evaluating an abstract. Various styles of indexing used in printed publications such as Index Medicus, the Engineering Index, and Chemical Abstracts are illustrated in the text; although the author is quick to note that printed tools are used much less today in favor of their online counterparts. In the online world, indexing has even greater importance in the effort to retrieve relevant data efficiently. Related concepts such as weighted indexing, linking of terms, and relational indicators are discussed as aids to precision. The idiosyncrasies of indexing special formats such as images and sounds and the Internet, as well as the use of computer-generated or automated indexing and abstracting, are also reviewed. The author admits that the Web has become so large and complex that it is beyond the scope of any single book to explain all of its components. He suggests the use of Web-based services such as The Extreme Web Searcher's Internet Handbook News and Updates http://extremesearcher.com/news.html or Search Engine Watch http://searchenginewatch.com to keep current with new developments.

Based on new research and years of practical experience, this guide presents the basic knowledge necessary to become a professional indexer. Synthesizing the thinking and experience of indexers and abstractors over the years, the book introduces readers to such fundamentals as the nature of information, the organization of information, vocabulary control, types of indexes and abstracts, evaluation of indexing, and the use of computers. A new chapter on indexing and the Internet has been added, as has a chapter that lists Web resources for indexers and abstractors. The work concludes with a discussion of the education, training, and job opportunities of the profession, as well as a look to the future. With its simple but thorough approach, this book provides readers with a broad overview of the professions, processes, and art of indexing and abstracting.

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.