Optimization and Machine Learning Based Single Document Extractive Summarization

Automatic text summarization is an important area of natural language processing, information retrieval, and artificial intelligence, particularly as the volume of digitally available text continues to expand. Optimization and Machine Learning-Based Single-Document Extractive Summarization provides a focused technical examination of computational approaches for identifying and selecting informative sentences from a single document. The book connects extractive summarization, machine learning, optimization, natural language processing, feature engineering, and text analysis within a structured computational framework.

The book introduces the fundamental principles of automatic text summarization and distinguishes extractive approaches from other forms of summarization. Extractive summarization focuses on selecting important sentences or textual units from an original document while preserving their original wording. This approach requires computational methods capable of assessing sentence importance, relevance, redundancy, and relationships within the source document.

A central focus is placed on feature-based analysis for single-document summarization. Textual features can provide useful information for estimating the relative importance of individual sentences. The book discusses concepts such as sentence position, term frequency, word distribution, sentence length, keyword occurrence, semantic relevance, cohesion, and other measurable characteristics that can contribute to sentence ranking and selection.