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BOOK SERIES IN NATURAL LANGUAGE PROCESSING
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John Benjamins’ NLP series (NLP-3)
http://www.wlv.ac.uk/~le1825/JB/series.htm
Book series editor Ruslan Mitkov
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AUTOMATIC SUMMARIZATION
Inderjeet Mani
John Benjamins Pub Co; ISBN: 1588110591 (hardcover), 1588110605 (paperback)
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With the explosion in the quantity of on-line text and multimedia
information in recent years, there has been a renewed interest in
automatic summarization. This book provides a systematic introduction to
the field, explaining basic definitions, the strategies used by human
summarizers, and automatic methods that leverage linguistic and
statistical knowledge to produce extracts and abstracts. Drawing from a
wealth of research in artificial intelligence, natural language
processing, and information retrieval, the book also includes detailed
assessments of evaluation methods and new topics such as multi-document
and multimedia summarization.
Previous automatic summarization books have been either collections of
specialized papers, or else authored books with only a chapter or two
devoted to the field as a whole. This is the first textbook on the
subject, based on teaching materials used in two one-semester
courses. To further help the student reader, the book includes detailed
case studies, accompanied by end-of-chapter reviews and an extensive
glossary.
The book is intended for students and researchers, as well as
information technology managers, librarians, and anyone else interested
in the subject.
TABLE OF CONTENTS
PREFACE
I. PRELIMINARIES
1. Introduction
2. Basic Notions for Summarization
3. Abstract Architecture for Summarization
4. Summarization Approaches
5. Current Applications
6. Conclusion
7. Review
II. PROFESSIONAL SUMMARIZING
1. Introduction
2. The stages of abstracting
3. Abstracting Strategies
4. Reading for Abstracting
5. Revision
6. Psychological Experiments
7. Structure of Empirical Abstracts
8. Conclusion
9. Review
III. EXTRACTION
1. Introduction
2. The Edmundsonian Paradigm
3. Corpus Based Sentence Extraction
3.1 General Considerations
3.2 Aspects of Learning Approaches
4. Coherence of Extracts
5. Conclusion
6. Review
IV. REVISION
1. Introduction
2. Shallow Coherence Smoothing
3. Full Revision to Improve Informativeness
3.1 Case Study: Full Revision
3.2 Related Work
3.3 Implications
4. Text Compaction
5. Conclusion
6. Review
V. DISCOURSE-LEVEL INFORMATION
1. Introduction
2. Text Cohesion
2.1 Introduction
2.2 Cohesion Graph Topology
2.3 Topic Characterization
3. Text Coherence
3.1 Introduction
3.2 Coherence Relations
3.3 Rhetorical Structure Theory (RST)
3.4 Rhetorical Structure and Cue Phrases
3.5 The Document Scheme, Revisited
4. Conclusion
5. Review
VI. ABSTRACTION
1. Introduction
2. Abstraction from Templates
2.1 Introduction
2.2 Case Study: Sketchy Scripts
2.3 Modern Information Extraction
3. Abstraction by Term Rewriting
4. Abstraction using Event Relations
5. Abstraction using a Concept Hierarchy
5.1. Domain Knowledge Base Activation
5.2. Generic Thesaurus Activation
6. Synthesis for Abstraction
6.1. Pretty printing
6.2. Graphical Output
6.3. Extraction
6.4. Generation for Synthesis
7. Conclusion
8. Review
VII. MULTI-DOCUMENT SUMMARIZATION
1. Introduction
2. Types of relationships across documents
3. MDS methods
3.1 Overview
3.2 Specific Approaches
4. Case Study: Biographical Summarization
4.1 Introduction
4.2 Example Architecture
4.3 Algorithm Steps
4.4 Bio Summarizer Components
4.5 Assessment
5. Conclusion
6. Review
VIII. MULTIMEDIA SUMMARIZATION
1. Introduction
2. Dialog Summarization
3. Summarization of Video
4. Summarization of Diagrams
5. Automatic Multimedia Briefing Generation
6. Conclusion
7. Review
IX. EVALUATION
1. Introduction
2. Intrinsic Methods
2.1 Assessing Agreement Between Subjects
2.2 Quality
2.3 Informativeness
2.4 Component-level tests
3. Extrinsic Methods
3.1 Relevance Assessment
3.2 Reading Comprehension
3.3 Presentation Strategies
3.4 Mature System Evaluation
4. Conclusion
5. Review
X. POSTSCRIPT
REFERENCES
INDEX
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