Extractive and Abstractive Summarization for Indian Languages and Dialects

Extractive and Abstractive Summarization for Indian Languages and Dialects examines natural language processing approaches for automatically generating concise summaries from textual content across diverse Indian linguistic contexts. The book focuses on two major summarization paradigms: extractive methods, which identify and select important information from existing text, and abstractive methods, which generate summaries using learned language representations. It introduces key concepts in natural language processing, text preprocessing, linguistic representation, sentence selection, text generation, and language modeling. Particular attention is given to the challenges of processing Indian languages and dialects, including linguistic diversity, morphological variation, limited resources, vocabulary differences, and language-specific characteristics. The discussion considers computational approaches for multilingual and low-resource text summarization, along with methods for evaluating the quality and effectiveness of generated summaries. By connecting extractive and abstractive techniques with Indian language processing, this book provides a technical foundation for students, researchers, computational linguists, data scientists, and professionals working in artificial intelligence, machine learning, natural language processing, multilingual computing, and language technologies.