Automatic Summarization (Book)


Automatic Summarization (2011) .. by Ani Nenkova & Kathleen McKeown


Contents

1 Introduction 104

1.1 Types of Summaries 104

1.2 How do Summarization Systems Work? 107

1.3 Evaluation Issues 114

1.4 Where Does Summarization Help? 115

1.5 Article Overview 117

2 Sentence Extraction: Determining Importance 120

2.1 Unsupervised Data-driven Methods 121

2.2 Machine Learning for Summarization 131

2.3 Sentence Selection vs. Summary Selection 134

2.4 Sentence Selection for Query-focused Summarization 136

2.5 Discussion 141

3 Methods Using Semantics and Discourse 143

3.1 Lexical Chains and Related Approaches 143

3.2 Latent Semantic Analysis 145

3.3 Coreference Information 146

3.4 Rhetorical Structure Theory 147

3.5 Discourse-motivated Graph Representations of Text 149

3.6 Discussion 150

4 Generation for Summarization 152

4.1 Sentence Compression 153

4.2 Information Fusion 162

4.3 Context Dependent Revisions 165

4.4 Information Ordering 168

4.5 Discussion 171

5 Genre and Domain Specific Approaches 173

5.1 Medical Summarization 174

5.2 Journal Article Summarization in Non-medical Domains 180

5.3 Email 184

5.4 Web Summarization 189

5.5 Summarization of Speech 193

5.6 Discussion 198

6 Intrinsic Evaluation 199

6.1 Precision and Recall 199

6.2 Relative Utility 201

6.3 DUC Manual Evaluation 202

6.4 Automatic Evaluation and ROUGE 204

6.5 Pyramid Method 204

6.6 Linguistic Quality Evaluation 205

6.7 Intrinsic Evaluation for Speech Summarization 206

7 Conclusions 210

References 216

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