Automatic text summarization

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Detalles Bibliográficos
Autor Principal:	Torres-Moreno, Juan-Manuel
Formato:	Libro
Lengua:	inglés
Datos de publicación:	Londres : ISTE, 2014
Edición:	1st ed.
Temas:	MINERÍA DE DATOS sumarización
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

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