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Synopsis
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.
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Optimization and Machine Learning Based Single Document Extractive Summarization
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Optimization and Machine Learning Based Single Document Extractive Summarization
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Optimization and Machine Learning Based Single Document Extractive Summarization (Paperback)
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Paperback. Condition: new. Paperback. 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. Optimization and Machine Learning-Based Single-Document Extractive Summarization examines computational methods for selecting informative sentences from individual documents. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9798240808401
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Optimization and Machine Learning Based Single Document Extractive Summarization (Paperback)
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Paperback. Condition: new. Paperback. 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. Optimization and Machine Learning-Based Single-Document Extractive Summarization examines computational methods for selecting informative sentences from individual documents. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798240808401
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Optimization and Machine Learning Based Single Document Extractive Summarization
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9798240808401
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Optimization and Machine Learning Based Single Document Extractive Summarization
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Taschenbuch. Condition: Neu. Optimization and Machine Learning Based Single Document Extractive Summarization | Maddur Mami | Taschenbuch | Englisch | 2026 | Pippet Sky | EAN 9798240808401 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 136294091
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