000 06152cam a2200673Ma 4500
001 ocn892044728
003 OCoLC
005 20171026104719.0
006 m o d
007 cr cnu---unuuu
008 141003s2014 enk ob 001 0 eng d
020 _a9781119044147
_q(electronic bk.)
020 _a1119044146
_q(electronic bk.)
020 _a1322166625
_q(electronic bk.)
020 _a9781322166629
_q(electronic bk.)
020 _a9781119004752
_q(electronic bk.)
020 _a1119004756
_q(electronic bk.)
020 _a1848216688
020 _a9781848216686
020 _z9781848216686
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035 _a(OCoLC)892044728
_z(OCoLC)959423368
040 _aIDEBK
_beng
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049 _aMAIN
050 4 _aZ695.92
072 7 _aLAN
_x025000
_2bisacsh
082 0 4 _a025.4/10285
_223
100 1 _aTorres-Moreno, Juan-Manuel.
245 1 0 _aAutomatic Text Summarization /
_cJuan-Manuel Torres-Moreno.
_h[electronic resource]
260 _aLondon :
_bISTE ;
_aHoboken, NJ :
_bWiley,
_c2014.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aCognitive science and knowledge management series
504 _aIncludes bibliographical references and index.
505 0 _aTitle Page; Copyright; Foreword by A. Zamora and R. Salvador; Foreword by H. Saggion; Notation; Introduction; PART 1: Foundations; 1 Why Summarize Texts?; 1.1. The need for automatic summarization; 1.2. Definitions of text summarization; 1.3. Categorizing automatic summaries; 1.4. Applications of automatic text summarization; 1.5. About automatic text summarization; 1.6. Conclusion; 2 Automatic Text Summarization: Some Important Concepts; 2.1. Processes before the process; 2.2. Extraction, abstraction or compression?; 2.3. Extraction-based summarization; 2.4. Abstract summarization.
505 8 _a2.5. Sentence compression and fusion2.6. The limits of extraction; 2.7. The evolution of automatic text summarization tasks; 2.8. Evaluating summaries; 2.9. Conclusion; 3 Single-document Summarization; 3.1. Historical approaches; 3.2. Machine learning approaches; 3.3. State-of-the-art approaches; 3.4. Latent semantic analysis; 3.5. Graph-based approaches; 3.6. DIVTEX: a summarizer based on the divergence of probability distribution; 3.7. CORTEX22; 3.8. ARTEX: another summarizer based on the vectorial model; 3.9. ENERTEX: a summarization system based on textual energy.
505 8 _a3.10. Approaches using rhetorical analysis3.11. Summarization by lexical chains; 3.12. Conclusion; 4 Guided Multi-Document Summarization; 4.1. Introduction; 4.2. The problems of multidocument summarization; 4.3. The DUC/TAC tasks for multidocument summarization and INEX Tweet Contextualization; 4.4. The taxonomy of multidocument summarization methods; 4.5. Some multi-document summarization systems and algorithms; 4.6. Update summarization; 4.7. Multi-document summarization by polytopes; 4.8. Redundancy; 4.9. Conclusion; 5 Multi and Cross-lingual Summarization.
505 8 _a5.1. Multilingualism, the web and automatic summarization5.2. Automatic multilingual summarization; 5.3. MEAD; 5.4. SUMMARIST; 5.5. COLUMBIA NEWSBLASTER; 5.6. NEWSEXPLORER; 5.7. GOOGLE NEWS; 5.8. CAPS; 5.9. Automatic cross-lingual summarization; 5.10. Conclusion; 6 Source and Domain-Specific Summarization; 6.1. Genre, specialized documents and automatic summarization; 6.2. Automatic summarization and organic chemistry; 6.3. Automatic summarization and biomedicine; 6.4. Summarizing court decisions; 6.5. Opinion summarization; 6.6. Web summarization; 6.7. Conclusion; 7 Text Abstracting.
505 8 _a7.1. 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.
520 _aTextual 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. --
_cEdited summary from book.
588 0 _aPrint version record.
650 0 _aAutomatic abstracting.
650 7 _aLANGUAGE ARTS & DISCIPLINES
_xLibrary & Information Science
_xGeneral.
_2bisacsh
650 7 _aAutomatic abstracting.
_2fast
_0(OCoLC)fst00822691
655 4 _aElectronic books.
776 0 8 _iPrint version:
_z9781848216686
830 0 _aCognitive science and knowledge management series.
856 4 0 _uhttp://onlinelibrary.wiley.com/book/10.1002/9781119004752
_zWiley Online Library
942 _2ddc
_cBK
999 _c207697
_d207697