Computational techniques for text summarization based on cognitive intelligence

Computational techniques for text summarization based on cognitive intelligence

The book is concerned with contemporary methodologies used for automatic text summarization. It proposes interesting approaches to solve well-known problems on text-summarization using computational intelligence (CI) techniques including cognitive approaches.

Detalles Bibliográficos
Otros Autores: Priya, V. (Professor of computer science and engineering), author (author), Umamaheswari, K. (Professor of information technology), author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Boca Raton ; Abingdon, Oxon : CRC Press 2023.
Edición:First edition
Materias:
Automatic abstracting.
Text processing (Computer science)
Computational intelligence.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009825851106719
Tabla de Contenidos:
  • Cover
  • Half Title
  • Title Page
  • Copyright Page
  • Table of Contents
  • About This Book
  • Preface
  • Chapter 1 Concepts of Text Summarization
  • 1.1 Introduction
  • 1.2 Need for Text Summarization
  • 1.3 Approaches to Text Summarization
  • 1.3.1 Extractive Summarization
  • 1.3.2 Abstractive Summarization
  • 1.4 Text Modeling for Extractive Summarization
  • 1.4.1 Bag-of-Words Model
  • 1.4.2 Vector Space Model
  • 1.4.3 Topic Representation Schemes
  • 1.4.4 Real-Valued Model
  • 1.5 Preprocessing for Extractive Summarization
  • 1.6 Emerging Techniques for Summarization
  • 1.7 Scope of the Book
  • References
  • Sample Code
  • Sample Screenshots
  • Chapter 2 Large-Scale Summarization Using Machine Learning Approach
  • 2.1 Scaling to Summarize Large Text
  • 2.2 Machine Learning Approaches
  • 2.2.1 Different Approaches for Modeling Text Summarization Problem
  • 2.2.2 Classification as Text Summarization
  • 2.2.2.1 Data Representation
  • 2.2.2.2 Text Feature Extraction
  • 2.2.2.3 Classification Techniques
  • 2.2.3 Clustering as Text Summarization
  • 2.2.4 Deep Learning Approach for Text Summarization
  • References
  • Sample Code
  • Chapter 3 Sentiment Analysis Approach to Text Summarization
  • 3.1 Introduction
  • 3.2 Sentiment Analysis: Overview
  • 3.2.1 Sentiment Extraction and Summarization
  • 3.2.1.1 Sentiment Extraction from Text
  • 3.2.1.2 Classification
  • 3.2.1.3 Score Computation
  • 3.2.1.4 Summary Generation
  • 3.2.2 Sentiment Summarization: An Illustration
  • Summarized Output
  • 3.2.3 Methodologies for Sentiment Summarization
  • 3.3 Implications of Sentiments in Text Summarization
  • Cognition-Based Sentiment Analysis and Summarization
  • 3.4 Summary
  • Practical Examples
  • Example 1
  • Example 2
  • Sample Code (Run Using GraphLab)
  • Example 3
  • References
  • Sample Code
  • Chapter 4 Text Summarization Using Parallel Processing Approach.
  • 4.1 Introduction
  • Parallelizing Computational Tasks
  • Parallelizing for Distributed Data
  • 4.2 Parallel Processing Approaches
  • 4.2.1 Parallel Algorithms for Text Summarization
  • 4.2.2 Parallel Bisection k-Means Method
  • 4.3 Parallel Data Processing Algorithms for Large-Scale Summarization
  • 4.3.1 Designing MapReduce Algorithm for Text Summarization
  • 4.3.2 Key Concepts in Mapper
  • 4.3.3 Key Concepts in Reducer
  • 4.3.4 Summary Generation
  • An Illustrative Example for MapReduce
  • Good Time: Movie Review
  • 4.4 Other MR-Based Methods
  • 4.5 Summary
  • 4.6 Examples
  • K-Means Clustering Using MapReduce
  • Parallel LDA Example (Using Gensim Package)
  • Sample Code: (Using Gensim Package)
  • Example: Creating an Inverted Index
  • Example: Relational Algebra (Table JOIN)
  • References
  • Sample Code
  • Chapter 5 Optimization Approaches for Text Summarization
  • 5.1 Introduction
  • 5.2 Optimization for Summarization
  • 5.2.1 Modeling Text Summarization as Optimization Problem
  • 5.2.2 Various Approaches for Optimization
  • 5.3 Formulation of Various Approaches
  • 5.3.1 Sentence Ranking Approach
  • 5.3.1.1 Stages and Illustration
  • 5.3.2 Evolutionary Approaches
  • 5.3.2.1 Stages
  • 5.3.2.2 Demonstration
  • 5.3.3 MapReduce-Based Approach
  • 5.3.3.1 In-Node Optimization Illustration
  • 5.3.4 Multi-objective-Based Approach
  • Summary
  • Exercises
  • References
  • Sample Code
  • Chapter 6 Performance Evaluation of Large-Scale Summarization Systems
  • 6.1 Evaluation of Summaries
  • 6.1.1 CNN Dataset
  • 6.1.2 Daily Mail Dataset
  • 6.1.3 Description
  • 6.2 Methodologies
  • 6.2.1 Intrinsic Methods
  • 6.2.2 Extrinsic Methods
  • 6.3 Intrinsic Methods
  • 6.3.1 Text Quality Measures
  • 6.3.1.1 Grammaticality
  • 6.3.1.2 Non-redundancy
  • 6.3.1.3 Reverential Clarity
  • 6.3.1.4 Structure and Coherence
  • 6.3.2 Co-selection-Based Methods.
  • 6.3.2.1 Precision, Recall, and F-score
  • 6.3.2.2 Relative Utility
  • 6.3.3 Content-Based Methods
  • 6.3.3.1 Content-Based Measures
  • 6.3.3.2 Cosine Similarity
  • 6.3.3.3 Unit Overlap
  • 6.3.3.4 Longest Common Subsequence
  • 6.3.3.5 N-Gram Co-occurrence Statistics: ROUGE
  • 6.3.3.6 Pyramids
  • 6.3.3.7 LSA-Based Measure
  • 6.3.3.8 Main Topic Similarity
  • 6.3.3.9 Term Significance Similarity
  • 6.4 Extrinsic Methods
  • 6.4.1 Document Categorization
  • 6.4.1.1 Information Retrieval
  • 6.4.1.2 Question Answering
  • 6.4.2 Summary
  • 6.4.3 Examples
  • Bibliography
  • Chapter 7 Applications and Future Directions
  • 7.1 Possible Directions in Modeling Text Summarization
  • 7.2 Scope of Summarization Systems in Different Applications
  • 7.3 Healthcare Domain
  • Future Directions for Medical Document Summarization
  • 7.4 Social Media
  • Challenges in Social Media Text Summarization
  • Domain Knowledge and Transfer Learning
  • Online Learning
  • Information Credibility
  • Applications of Deep Learning
  • Implicit and Explicit Information for Actionable Insights
  • 7.5 Research Directions for Text Summarization
  • 7.6 Further Scope of Research on Large-Scale Summarization
  • Conclusion
  • References
  • Appendix A: Python Projects and Useful Links on Text Summarization
  • Appendix B: Solutions to Selected Exercises
  • Index.

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