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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.
This book offers a thorough examination of the state-of-the-art methods to describe text summarization. For both extractive summarizing tasks and abstractive summary tasks, the reader will discover in-depth treatment of several methodologies utilizing Machine Learning (ML), Natural Language Processing (NLP), and data mining techniques. Additionally, it is shown how summarizing methodologies can be used in a variety of applications, including healthcare and social media domain along with the possible research directions and future scope.
This section will briefly cover the text summarization Machine Learning algorithms and the implementation aspects in Python with source code. Some of the algorithms and Python libraries available are explained below.
NLTK is an acronym for Natural Language Toolkit. It is the most commonly used Python package for handling human language data. It includes libraries for categorization, tokenization, stemming, tagging, parsing, and other text processing tasks. For text summarization, the NLTK employs the TF-IDF approach.
Gensim is a Python package that relies on other Python libraries such as NumPy and SciPy. It is based on corpus theory, vector theory, models, and sparse matrices. Genism implements the TextRank algorithm for text summarization.
Matthew Honnibal and Ines Montani, the creators of the software business Explosion, created SpaCy. It’s majorly used for research purposes. CNN models for part-of-speech tagging, dependency parsing, text categorization, and named entity recognition are some of SpaCy’s most notable features. Text summarization using deep learning models. Recurrent neural networks (RNNs), convolutional neural networks (CNNs), and sequence-to-sequence models are the most commonly used abstractive text summarization Deep Learning models.
Preface
About This Book
Concepts of Text Summarization
Large-Scale Summarization Using Machine Learning Approach
Sentiment Analysis Approach to Text Summarization
Text Summarization Using Parallel Processing Approach
Optimization Approaches for Text Summarization
Performance Evaluation of Large-Scale Summarization Systems
Applications and Future Directions
Ap