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Automatic Text Summarization /
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 t...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London : Hoboken, NJ :
ISTE ; Wiley,
2014.
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| Colección: | Cognitive science and knowledge management series.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Title 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.
- 2.5. 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.
- 3.10. 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.
- 5.1. 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.
- 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.