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. 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 is presented with reasonable amount of illustrations and examples convenient for the readers to understand and implement for their use. It is not to make readers understand what text summarization is, but for people to perform text summarization using various approaches. This also describes measures that can help to evaluate, determine, and explore the best possibilities for text summarization to analyse and use for any specific purpose. The illustration is based on social media and healthcare domain, which shows the possibilities to work with any domain for summarization. The new approach for text summarization based on cognitive intelligence is presented for further exploration in the field.
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.
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
V. Priya is presently working as an assistant professor in, the department of computer science and engineering, Dr. N. G. P. Institute of Technology, Coimbatore, India. Her areas of research include text summarization using map-reduce and optimization along with an application. She has taught courses such as big data, data warehousing, and mining, operating systems, data management, and analytics at undergraduate and graduate levels. She has published research papers in journals of national and international repute.
K. Umamaheswari is currently working as a professor and head of, the department of information technology, PSG College of Technology, India. She has more than twenty-five years of teaching experience and has published more than a hundred papers in journals and conferences of national and international repute. Her research interests include data mining, cognitive networks, text mining, and information retrieval. She is the senior editor for the National Journal of Technology and reviewers for many national and international journals.
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