Description
Book SynopsisThe 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. A better understanding of the cognitive basis of the summarization task is still an open research issue; an extent of its use in text summarization is highlighted for further exploration. With the ever-growing text, people in research have little time to spare for extensive reading, where summarized information helps for a better understanding of the context at a shorter time.
This book helps students and researchers to automatically summarize the text documents in an efficient and effective way. The computational approaches and the research techniques presented guides to achieve text summarization at ease. The summarized text generated supports readers to learn the context or the domain at a quicker pace. The book
Table of Contents
Preface
About This Book
1. Concepts of Text Summarization
2. Large-Scale Summarization Using Machine Learning Approach
3. Sentiment Analysis Approach to Text Summarization
4. Text Summarization Using Parallel Processing Approach
5. Optimization Approaches for Text Summarization
6. Performance Evaluation of Large-Scale Summarization Systems
7. Applications and Future Directions
Appendix A: Python Projects and Useful Links on Text Summarization
Appendix B: Solutions to Selected Exercises
Index
