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| 003 | OCoLC | ||
| 005 | 20240523125537.0 | ||
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| 007 | cr cnu---unuuu | ||
| 008 | 141003s2014 enk ob 001 0 eng d | ||
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| 020 | _a9781848216686 | ||
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_a(OCoLC)892044728 _z(OCoLC)959423368 _z(OCoLC)961583898 _z(OCoLC)992885994 _z(OCoLC)1055362450 _z(OCoLC)1081204344 _z(OCoLC)1228558258 |
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_aLAN _x025000 _2bisacsh |
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| 082 | 0 | 4 |
_a025.4/10285 _223 |
| 049 | _aMAIN | ||
| 100 | 1 | _aTorres-Moreno, Juan-Manuel. | |
| 245 | 1 | 0 |
_aAutomatic Text Summarization / _cJuan-Manuel Torres-Moreno. |
| 260 |
_aLondon : _bISTE ; _aHoboken, NJ : _bWiley, _c2014. |
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| 300 | _a1 online resource | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aCognitive science and knowledge management series | |
| 588 | 0 | _aPrint version record. | |
| 504 | _aIncludes bibliographical references and index. | ||
| 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. |
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| 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. | |
| 590 |
_aJohn Wiley and Sons _bWiley Online Library: Complete oBooks |
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| 650 | 0 | _aAutomatic abstracting. | |
| 650 | 6 | _aR�esum�es automatiques. | |
| 650 | 7 |
_aLANGUAGE ARTS & DISCIPLINES _xLibrary & Information Science _xGeneral. _2bisacsh |
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| 650 | 7 |
_aAutomatic abstracting _2fast |
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| 758 |
_ihas work: _aAutomatic text summarization (Text) _1https://id.oclc.org/worldcat/entity/E39PCGyfrhTGqw3fBDkBYgtPcd _4https://id.oclc.org/worldcat/ontology/hasWork |
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_iPrint version: _z9781848216686 |
| 830 | 0 | _aCognitive science and knowledge management series. | |
| 856 | 4 | 0 | _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119004752 |
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