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ntil now there has been no state-of-the-art collection of themost important writings in automatic text summarization. This bookpresents the key developments in the field in an integrated frameworkand suggests future research areas. With the rapid growth of the World Wide Web and electronic information services, information is becoming available on-line at an incredible rate. One result is the oft-decried information overload. No one has time to read everything, yet we often have to make critical decisions based on what we are able to assimilate. The technology of automatic text summarization is becoming indispensable for dealing with this problem. Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task. Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections: Classical Approaches, Corpus-Based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas. Contributors D. A. Adams, C. Aone, R. Barzilay, E. Bloedorn, B. Boguraev, R. Brandow, C. Buckley, F. Chen, M. J. Chrzanowski, H. P. Edmundson, M. Elhadad, T. Firmin, R. P. Futrelle, J. Gorlinsky, U. Hahn, E. Hovy, D. Jang, K. Sparck Jones, G. M. Kasper, C. Kennedy, K. Kukich, J. Kupiec, B. Larsen, W. G. Lehnert, C. Lin, H. P. Luhn, I. Mani, D. Marcu, M. Maybury, K. McKeown, A. Merlino, M. Mitra, K. Mitze, M. Moens, A. H. Morris, S. H. Myaeng, M. E. Okurowski, J. Pedersen, J. J. Pollock, D. R. Radev, G. J. Rath, L. F. Rau, U. Reimer, A. Resnick, J. Robin, G. Salton, T. R. Savage, A. Singhal, G. Stein, T. Strzalkowski, S. Teufel, J. Wang, B. Wise, A. Zamora
Automatic Summarization is a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and genre specific summarization and for evaluation of summarization
While the availability of electronic documents increases exponentially with advancing technology, the time spent to process this wealth of resourceful information decreases. Content analysis and information extraction must be aided by summarization methods to quickly parcel pieces of interest and allow for succinct user familiarization in a simple, efficient manner. Trends and Applications of Text Summarization Techniques is a pivotal reference source that explores the latest approaches of document summarization including update, multi-lingual, and domain-oriented summarization tasks and examines their current real-world applications in multiple fields. Featuring coverage on a wide range of topics such as parallel construction, social network integration, and evaluation metrics, this book is ideally designed for information technology practitioners, computer scientists, bioinformatics analysts, business managers, healthcare professionals, academicians, researchers, and students.
Information retrieval (IR) is considered to be the science of searching for information from a variety of information sources related to texts, images, sounds, or multimedia. With the rise of the internet and digital databases, updated information retrieval methodologies are essential to ensure the continued facilitation and enhancement of information exchange. Critical Approaches to Information Retrieval Research is a critical scholarly publication that provides multidisciplinary examinations of theoretical innovations and methods in information retrieval technologies including search and storage applications for data, text, image, sound, document, and video retrieval. Featuring a wide range of topics including data mining, machine learning, and ontology, this book is ideal for librarians, software engineers, data scientists, professionals, researchers, information engineers, scientists, practitioners, and academicians working in the fields of computer science, information technology, information and communication sciences, education, health, library, and more.
Natural language processing (NLP) is a branch of artificial intelligence that has emerged as a prevalent method of practice for a sizeable amount of companies. NLP enables software to understand human language and process complex data that is generated within businesses. In a competitive market, leading organizations are showing an increased interest in implementing this technology to improve user experience and establish smarter decision-making methods. Research on the application of intelligent analytics is crucial for professionals and companies who wish to gain an edge on the opposition. The Handbook of Research on Natural Language Processing and Smart Service Systems is a collection of innovative research on the integration and development of intelligent software tools and their various applications within professional environments. While highlighting topics including discourse analysis, information retrieval, and advanced dialog systems, this book is ideally designed for developers, practitioners, researchers, managers, engineers, academicians, business professionals, scholars, policymakers, and students seeking current research on the improvement of competitive practices through the use of NLP and smart service systems.
With the explosion in the quantity of on-line text and multimedia information in recent years, there has been a renewed interest in automatic summarization. This book provides a systematic introduction to the field, explaining basic definitions, the strategies used by human summarizers, and automatic methods that leverage linguistic and statistical knowledge to produce extracts and abstracts. Drawing from a wealth of research in artificial intelligence, natural language processing, and information retrieval, the book also includes detailed assessments of evaluation methods and new topics such as multi-document and multimedia summarization. Previous automatic summarization books have been either collections of specialized papers, or else authored books with only a chapter or two devoted to the field as a whole. This is the first textbook on the subject, developed based on teaching materials used in two one-semester courses. To further help the student reader, the book includes detailed case studies, accompanied by end-of-chapter reviews and an extensive glossary.Audience: students and researchers, as well as information technology managers, librarians, and anyone else interested in the subject.
In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence. The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources. This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas.
The prevalence of digital documentation presents some pressing concerns for efficient information retrieval in the modern age. Readers want to be able to access the information they desire without having to search through a mountain of unrelated data, so algorithms and methods for effectively seeking out pertinent information are of critical importance. Innovative Document Summarization Techniques: Revolutionizing Knowledge Understanding evaluates some of the existing approaches to information retrieval and summarization of digital documents, as well as current research and future developments. This book serves as a sounding board for students, educators, researchers, and practitioners of information technology, advancing the ongoing discussion of communication in the digital age.
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.