Automatic Text Summarization - Torres-Moreno, Juan-Manuel. (London : ISTE ; Hoboken, NJ : Wiley, 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. This book can help to process this ever-increasing, difficult-to-handle, mass of information. It 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. It 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. --
7.1. Abstraction-based automatic summarization7.2. Systems using natural language generation; 7.3. An abstract generator using information extraction; 7.4. Guided summarization and a fully abstractive approach; 7.5. Abstraction-based summarization via conceptual graphs; 7.6. Multisentence fusion; 7.7. Sentence compression; 7.8. Conclusion; 8 Evaluating Document Summaries; 8.1. How can summaries be evaluated?; 8.2. Extrinsic evaluations; 8.3. Intrinsic evaluations; 8.4. TIPSTER SUMMAC evaluation campaigns; 8.5. NTCIR evaluation campaigns; 8.6. DUC/TAC evaluation campaigns.
Includes bibliographical references and index.
Print version record.
Keywords: Automatic abstracting; LANGUAGE ARTS & DISCIPLINES -- Library & Information Science -- General; Automatic abstracting; Electronic books