Automatic text summarization
| Autor Principal: | |
|---|---|
| Formato: | Libro |
| Lengua: | inglés |
| Datos de publicación: |
Londres :
ISTE,
2014
|
| Edición: | 1st ed. |
| Temas: | |
| Acceso en línea: | Consultar en el Cátalogo |
| Notas: | Incluye índice y bibliografía. |
| Descripción Física: | xxiii, 348 p. : il. |
| ISBN: | 9781848216686 |
Tabla de Contenidos:
- 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
- 2.1.1. Sentence-Term Matrix: The Vector Space Model (VSM) Model
- 2.2. Extraction, Abstraction Or Compression?
- 2.3. Extraction-Based Summarization
- 2.3.1. Surface-Level Algorithms
- 2.3.2. Intermediate-Level Algorithms
- 2.3.3. Deep Parsing Algorithms
- 2.4. Abstract Summarization
- 2.4.1. FRUMP
- 2.4.2. Information Extraction And Abstract Generation
- 2.5. Sentence Compression And Fusion
- 2.5.1. Sentence Compression
- 2.5.2. Multisentence Fusion
- 2.6. The Limits Of Extraction
- 2.6.1. Cohesion And Coherence
- 2.6.2. The HexTAC Experiment
- 2.7. The Evolution Of Text Summarization Tasks
- 2.7.1. Traditional Tasks
- 2.7.2. Current And Future Problems
- 2.8. Evaluating Summaries
- 2.9. Conclusion
- 3. Single-Document Summarization
- 3.1. Historical Approaches
- 3.1.1. Luhn’s Automatic Creation Of Literature Abstracts
- 3.1.2. The Luhn Algorithm
- 3.1.3. Edmundson’s Linear Combination
- 3.1.4. Extracts By Elimination
- 3.2. Machine Learning Approaches
- 3.2.1. Machine Learning Parameters
- 3.3. State-Of-The-Art Approaches
- 3.4. Latent Semantic Analysis
- 3.4.1. Singular Value Decomposition (SVD)
- 3.4.2. Sentence Weighting By SVD
- 3.5. Graph-Based Approaches
- 3.5.1. PAGERANK And SNA Algorithms
- 3.5.2. Graphs And Automatic Text Summarization
- 3.5.3. Constructing The Graph
- 3.5.4. Sentence Weighting
- 3.6. DIVTEX: A Summarizer Based On The Divergence Of Probability Distribution
- 3.7. CORTEX
- 3.7.1. Frequential Measures
- 3.7.2. Hamming Measures
- 3.7.3. Mixed Measures
- 3.7.4. Decision Algorithm
- 3.8. ARTEX
- 3.9. ENERTEX
- 3.9.1. Spins And Neural Networks
- 3.9.2. The Textual Energy Similarity Measure
- 3.9.3. Summarization By Extraction And Textual Energy
- 3.10. Approaches Using Rhetorical Analysis
- 3.11. Lexical Chains
- 3.12. Conclusion
- 4. Guided Multi-Document Summarization
- 4.1. Introduction
- 4.2. The Problems Of Multidocument Summarization
- 4.3. DUC/TAC & INEX Tweet Contextualization
- 4.4. The Taxonomy Of MDS Methods
- 4.4.1. Structure Based
- 4.4.2. Vector Space Model Based
- 4.4.3. Graph Based
- 4.5. Some Multi-Document Summarization Systems And Algorithms
- 4.5.1. SUMMONS
- 4.5.2. Maximal Marginal Relevance
- 4.5.3. A Multidocument Biography Summarization System
- 4.5.4. Multi-Document ENERTEX
- 4.5.5. MEAD
- 4.5.6. CATS
- 4.5.7. SUMUM And SUMMA
- 4.5.8. NEO-CORTEX
- 4.6. Update Summarization
- 4.6.1. Update Summarization Pilot Task At DUC 2007
- 4.6.2. Update Summarization Task At TAC 2008 And 2009
- 4.6.3. A Minimization-Maximization Approach
- 4.6.4. The ICSI System At TAC 2008 And 2009
- 4.6.5. The CBSEAS System At TAC
- 4.7. Multidocument Summarization By Polytopes
- 4.8. Redundancy
- 4.9. Conclusion
- Part 2. Emerging Systems
- 5. Multi And Cross-Lingual Summarization
- 5.1. Multilingualism, The Web And Automatic Summarization
- 5.2. 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
- 5.9.1. The Quality Of Machine Translation
- 5.9.2. A Graph-Based Cross-Lingual Summarizer
- 5.10. Conclusion
- 6. Source And Domain-Specific Summarization
- 6.1. Genre, Specialized Documents And Automatic Summarization
- 6.2. Automatic Summarization And Organic Chemistry
- 6.2.1. YACHS2
- 6.3. Automatic Summarization And Biomedicine
- 6.3.1. SUMMTERM
- 6.3.2. A Linguistic-Statistical Approach
- 6.4. Summarizing Court Decisions
- 6.5. Opinion Summarization
- 6.5.1. CBSEAS At TAC 2008 Opinion Task
- 6.6.Web Summarization
- 6.6.1. Web Page Summarization
- 6.6.2. OCELOT And The Statistical Gist
- 6.6.3. Multitweet Summarization
- 6.6.4. Email Summarization
- 6.7. Conclusion
- 7. Text Abstracting
- 7.1. Abstraction-Based Automatic Summarization
- 7.2. 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
- 7.6.1. Multisentence Fusion Via Graphs
- 7.6.2. Graphs And Keyphrase Extraction: The Takahé System
- 7.7. Sentence Compression
- 7.7.1. Symbolic Approaches
- 7.7.2. Statistical Approaches
- 7.7.3. A Statistical-Linguistic Approach
- 7.8. Conclusion
- 8. Evaluating Document Summaries
- 8.1. How Can Summaries Be Evaluated?
- 8.2. Extrinsic Evaluations
- 8.3. Intrinsic Evaluations
- 8.3.1. The Baseline Summary
- 8.4. TIPSTER SUMMAC Evaluation Campaigns
- 8.4.1. Ad Hoc Task
- 8.4.2. Categorization Task
- 8.4.3. Question-Answering Task
- 8.5. NTCIR Evaluation Campaigns
- 8.6. DUC/TAC Evaluation Campaigns
- 8.6.1. Manual Evaluations
- 8.7. CLEF-INEX Evaluation Campaigns
- 8.8. Semi-Automatic Methods For Evaluating Summaries
- 8.8.1. Level Of Granularity: The Sentence
- 8.8.2. Level Of Granularity: Words
- 8.9. Automatic Evaluation Via Information Theory
- 8.9.1. Divergence Of Probability Distribution
- 8.9.2. FRESA
- 8.10. Conclusion
- Conclusion
- Appendix 1. Information Retrieval, NLP And ATS
- Appendix 2. Automatic Text Summarization Resources
- Bibliography
- Index