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Multi-source, Multilingual Information Extraction and Summarization

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Cover of 'Multi-source, Multilingual Information Extraction and Summarization'

Table of Contents

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    Book Overview
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    Chapter 1 Automatic Text Summarization: Past, Present and Future
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    Chapter 2 Information Extraction: Past, Present and Future
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    Chapter 3 Learning to Match Names Across Languages
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    Chapter 4 Computational Methods for Name Normalization Using Hypocoristic Personal Name Variants
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    Chapter 5 Entity Linking: Finding Extracted Entities in a Knowledge Base
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    Chapter 6 A Study of the Effect of Document Representations in Clustering-Based Cross-Document Coreference Resolution
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    Chapter 7 Interactive Topic Graph Extraction and Exploration of Web Content
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    Chapter 8 Predicting Relevance of Event Extraction for the End User
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    Chapter 9 Open-Domain Multi-Document Summarization via Information Extraction: Challenges and Prospects
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    Chapter 10 Generating Update Summaries: Using an Unsupervized Clustering Algorithm to Cluster Sentences
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    Chapter 11 Multilingual Statistical News Summarization
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    Chapter 12 A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization
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    Chapter 13 Improving Speech-to-Text Summarization by Using Additional Information Sources
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    Chapter 14 Multi-Document Summarization Techniques for Generating Image Descriptions: A Comparative Analysis
Attention for Chapter 8: Predicting Relevance of Event Extraction for the End User
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Citations

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Chapter title
Predicting Relevance of Event Extraction for the End User
Chapter number 8
Book title
Multi-source, Multilingual Information Extraction and Summarization
Published by
Springer, Berlin, Heidelberg, January 2013
DOI 10.1007/978-3-642-28569-1_8
Book ISBNs
978-3-64-228568-4, 978-3-64-228569-1
Authors

Silja Huttunen, Arto Vihavainen, Mian Du, Roman Yangarber

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer 1 50%
Student > Doctoral Student 1 50%
Readers by discipline Count As %
Computer Science 2 100%

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