Automatic Text Summarization

Name: Automatic Text Summarization
Brand: Wiley-ISTE
Price: 139.99 EUR
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Juan-Manuel TORRES-MORENO(Herausgeber*in)

Wiley-ISTE (Verlag)

Erschienen am 25. September 2014

320 Seiten

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978-1-119-04414-7 (ISBN)

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Inhalt

Cover Page
Half-title Page
Title page
Copyright page
Contents
Foreword by A. Zamora and R. Salvador
Foreword
The need to identify important information
The problem of information storage
Automatic size reduction
The future
Foreword by H. Saggion
Automatic Text Summarization
Juan-Manual Torres-Moreno
Notation
Introduction
The need to summarize texts
The summarization process
Automatic text summarization
About this book
PART 1: Foundations
1: Why Summarize Texts?
1.1. The need for automatic summarization
1.2. Definitions of text summarization
1.3. Categorizing automatic summaries
1.4. Applications of automatic text summarization
1.5. About automatic text summarization
1.6. Conclusion
2: Automatic Text Summarization: Some Important Concepts
2.1. Processes before the process
2.1.1. Sentence-term matrix: the vector space model (VSM) model
2.2. Extraction, abstraction or compression?
2.3. Extraction-based summarization
2.3.1. Surface-level algorithms
2.3.2. Intermediate-level algorithms
2.3.3. Deep parsing algorithms
2.4. Abstract summarization
2.4.1. FRUMP or the temptation to understand
2.4.2. Information extraction and abstract generation
2.5. Sentence compression and fusion
2.5.1. Sentence compression
2.5.2. Multisentence fusion
2.6. The limits of extraction
2.6.1. Cohesion and coherence
2.6.2. The HexTAC experiment
2.7. The evolution of automatic text summarization tasks
2.7.1. Traditional tasks
Single-document summarization
Guided summarization
Multidocument summarization
Multilingual summarization
2.7.2. Current and future problems
Summarization based on the source of the document
Specialized-domain summarization
Update summarization
Sentence compression and multi-sentence fusion
Semantic summarization
Opinion summarization
Multi and cross-lingual summarization
Ultra-summarization
Tweet summarization (short texts in microblogs)
Multimedia summarization
Abstract generation
2.8. Evaluating summaries
2.9. Conclusion
3: Single-document Summarization
3.1. Historical approaches
3.1.1. H.P. Luhn's Automatic Creation of Literature Abstracts
3.1.2. The Luhn algorithm
3.1.2.1. Preprocessing
3.1.2.2. Sentence weighting
3.1.3. Edmundson's linear combination
Edmundson's algorithm
3.1.4. Extracts by elimination
3.2. Machine learning approaches
3.2.1. Machine learning parameters
3.3. State-of-the-art approaches
3.4. Latent semantic analysis
3.4.1. Singular value decomposition (SVD)
3.4.2. Sentence weighting by SVD
3.5. Graph-based approaches
3.5.1. PAGERANK and SNA algorithms
3.5.2. Graphs and automatic text summarization
3.5.3. Constructing the graph
3.5.4. Sentence weighting
3.5.4.1. LEXRANK
3.5.4.2. TEXTRANK
3.6. DIVTEX: a summarizer based on the divergence of probability distribution
3.7. CORTEX 22
3.7.1. Frequential measures
3.7.2. Hamming measures
3.7.3. Mixed measures
3.7.4. Decision algorithm
3.8. ARTEX: another summarizer based on the vectorial model
3.9. ENERTEX: a summarization system based on textual energy
3.9.1. Spins and neural networks
3.9.2. The textual energy similarity measure
3.9.3. Summarization by extraction and textual energy
Example
3.10. Approaches using rhetorical analysis
3.11. Summarization by lexical chains
3.12. Conclusion
4: Guided Multi-Document Summarization
4.1. Introduction
4.2. The problems of multidocument summarization
4.3. The DUC/TAC tasks for multidocument summarization and INEX Tweet Contextualization
4.4. The taxonomy of multidocument summarization methods
4.4.1. Structure based
4.4.2. Vector space model based
4.4.3. Graph based
4.5. Some multi-document summarization systems and algorithms
4.5.1. SUMMONS
4.5.2. Maximal marginal relevance
4.5.3. A multi-document biography summarization system
4.5.4. Multi-document ENERTEX
4.5.5. MEAD
4.5.6. CATS
4.5.7. SUMUM and SUMMA
4.5.8. NEO-CORTEX
Global similarity
Local overlap
Sentence weighting
Postprocessing
4.6. Update summarization
4.6.1. Update summarization pilot task at DUC 2007
4.6.2. Update summarization task at TAC 2008 and 2009
4.6.3. A minimization-maximization approach
4.6.3.1. Relevance to the topic
4.6.3.2. Eliminating redundancy
4.6.3.3. Sentence weighting
4.6.4. The ICSI system at TAC 2008 and 2009
4.6.5. The CBSEAS system at TAC
4.7. Multi-document summarization by polytopes
4.8. Redundancy
4.9. Conclusion
PART 2: Emerging systems
5: Multi and Cross-lingual Summarization
5.1. Multilingualism, the web and automatic summarization
5.2. Automatic multilingual summarization
5.3. MEAD
5.4. SUMMARIST
5.5. COLUMBIA NEWSBLASTER
5.6. NEWSEXPLORER
5.7. GOOGLE NEWS
5.8. CAPS
5.9. Automatic cross-lingual summarization
5.9.1. The quality of machine translation
5.9.1.1. Levenshtein distance
5.9.1.2. BLEU score
5.9.1.3. NIST score
5.9.2. A graph-based cross-lingual English-French summarizer
5.9.2.1. Predicting translation quality
5.9.2.2. Including translation quality scores
5.10. Conclusion
6: Source and Domain-Specific Summarization
6.1. Genre, specialized documents and automatic summarization
6.2. Automatic summarization and organic chemistry
6.2.1. YACHS2
6.2.1.1. Preprocessing
6.2.1.2. Sentence weighting
6.3. Automatic summarization and biomedicine
6.3.1. SUMMTERM
The lexical databases WordNet and EuroWordNet
Yet Another Term Extractor (YATE)
Sentence weighting
6.3.2. A linguistic-statistical approach
6.3.2.1. DISICOSUM
6.3.2.2. A hybrid summarizer
6.4. Summarizing court decisions
6.5. Opinion summarization
6.5.1. CBSEAS at TAC 2008 opinion task
6.6. Web summarization
6.6.1. Web page summarization
6.6.2. OCELOT and the statistical gist
6.6.3. Multitweet summarization
6.6.3.1. Graph-based multitweet summarization
6.6.4. Email summarization
6.7. Conclusion
7: Text Abstracting
7.1. Abstraction-based automatic summarization
7.2. Systems using natural language generation
7.3. An abstract generator using information extraction
7.4. Guided summarization and a fully abstractive approach
7.5. Abstraction-based summarization via conceptual graphs
7.6. Multisentence fusion
7.6.1. Multisentence fusion via graphs
7.6.2. Graphs and keyphrase extraction: the TAKAHÉ system
7.7. Sentence compression
7.7.1. Symbolic approaches
7.7.2. Statistical approaches
7.7.3. A statistical-linguistic approach
7.8. Conclusion
8: Evaluating Document Summaries
8.1. How can summaries be evaluated?
8.2. Extrinsic evaluations
8.3. Intrinsic evaluations
8.3.1. The baseline summary
8.4. TIPSTER SUMMAC evaluation campaigns
8.4.1. Ad hoc task
8.4.2. Categorization task
8.4.3. Question-answering task
8.5. NTCIR evaluation campaigns
8.6. DUC/TAC evaluation campaigns
8.6.1. Manual evaluations
8.7. CLEF-INEX evaluation campaigns
8.8. Semi-automatic methods for evaluating summaries
8.8.1. Level of granularity: the sentence
8.8.2. Level of granularity: words
8.8.2.1. Recall-oriented understudy for Gisting evaluation
8.8.2.2. Basic Elements
8.8.2.3. PYRAMID
8.9. Automatic evaluation via information theory
8.9.1. Divergence of probability distribution
8.9.2. FRESA
8.10. Conclusion
Conclusion
Appendix 1: Information Retrieval, NLP and Automatic Text Summarization
A.1. Text preprocessing
A.1.1. Text splitting
Sentence splitting
Tokenization
A.1.2. Token normalization
Lemmatization
Stemming
Normalization of graphic forms
A.1.3. Filtering
A.1.4. Named entities
A.1.5. POS-tagging
A.2. The vector space model
A.2.1. A matrix representation
A.2.2. The similarity of documents
Cosine similarity
Euclidean distance
Jaccard similarity
Dice coefficient
A.3. Precision, recall, F-measure and accuracy
Appendix 2: Automatic Text Summarization Resources
Bibliography
Index

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