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 automati
Automatic Summarization
โ Scribed by Nenkova A., McKeown K.
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โฆ Synopsis
Foundations and Trends in Information Retrieval Vol. 5, Nos. 2โ3 (2011) 103โ233
Contents:IntroductionTypes of Summaries
How do Summarization Systems Work?
Evaluation Issues
Where Does Summarization Help?
Article OverviewSentence Extraction: Determining Importance
Unsupervised Data-driven Methods
Machine Learning for Summarization
Sentence Selection vs. Summary Selection
Sentence Selection for Query-focused Summarization
DiscussionMethods Using Semantics and Discourse
Lexical Chains and Related Approaches
Latent Semantic Analysis
Coreference Information
Rhetorical Structure Theory
Discourse-motivated Graph Representations of Text
DiscussionGeneration for Summarization
Sentence Compression
Information Fusion
Context Dependent Revisions
Information Ordering
DiscussionGenre and Domain Speci๏ฌc Approaches
Medical Summarization
Journal Article Summarization in Non-medical Domains
Web Summarization
Summarization of Speech
DiscussionIntrinsic Evaluation
Precision and Recall
Relative Utility
DUC Manual Evaluation
Automatic Evaluation and ROUGE
Pyramid Method
Linguistic Quality Evaluation
Intrinsic Evaluation for Speech SummarizationConclusions
โฆ Subjects
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