Multi-source, Multilingual Information Extraction and Summarization

• Corporate Author: SpringerLink (Online service)
• Other Authors: Poibeau, Thierry. (Editor), Saggion, Horacio. (Editor), Piskorski, Jakub. (Editor), Yangarber, Roman. (Editor)
• Format: Electronic
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Series: Theory and Applications of Natural Language Processing,
• Subjects: Computer science.; Data mining.; Information storage and retrieval systems.; Computational linguistics.; Semantics.; Computer Science.; Data Mining and Knowledge Discovery.; Computational Linguistics.; Information Storage and Retrieval.; Information Systems Applications (incl. Internet).
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-28569-1

Information extraction (IE) and text summarization (TS) are powerful technologies for finding relevant pieces of information in text and presenting them to the user in condensed form. The ongoing information explosion makes IE and TS critical for successful functioning within the information society...

Table of Contents:

• Part I Background and Fundamentals
• 1.Automatic Text Summarization: Past, Present and Future. Horacio Saggion and Thierry Poibeau
• Information Extraction: Past, Present and Future. Jakub Piskorski and Roman Yangarber
• Part II Named Entity in a Multilingual Context
• Learning to Match Names Across Languages. Inderjeet Mani, Alex Yeh, and Sherri Condon
• Computational Methods for Name Normalization Using Hypocoristic Personal Name Variants. Patricia Driscoll
• Entity Linking: Finding Extracted Entities in a Knowledge Base. Delip Rao, Paul McNamee, and Mark Dredze
• A Study of the Effect of Document Representations in Clustering-based Cross-document Coreference Resolution. Horacio Saggion
• Part III Information Extraction
• Interactive Topic Graph Extraction and Exploration of Web Content. G<U+00fc>nter Neumann and Sven Schmeier
• Predicting Relevance of Event Extraction for the End User. Silja Huttunen, Arto Vihavainen, Mian Du, and Roman Yangarber
• Open-domain Multi-Document Summarization via Information Extraction: Challenges and Prospects. Heng Ji, Benoit Favre, Wen-Pin Lin, Dan Gillick, Dilek Hakkani-Tur, and Ralph Grishman
• Part IV Multi-document Summarization
• Generating Update Summaries: Using an Unsupervized Clustering Algorithm to Cluster Sentences. Aurľien Bossard
• Multilingual Statistical News Summarization. Mijail Kabadjov, Josef Steinberger and Ralf Steinberger
• A Bottom-up Approach to Sentence Ordering for Multi-document Summarization. Danushka Bollegala, Naoaki Okazaki, and Mitsuru Ishizuka
• Improving Speech-to-Text Summarization by Using Additional Information Sources. Ricardo Ribeiro and David Martins de Matos
• Multi-Document Summarization Techniques for Generating Image Descriptions: A Comparative Analysis. Ahmet Aker, Laura Plaza, Elena Lloret, and Robert Gaizauskas
• Index.