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Multi-source, Multilingual Information Extraction and Summarization

By Thierry Poibeau , Horacio Saggion , Jakub Piskorski , Roman Yangarber

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  • ISBN13: 978-3-6422-8568-4
  • 343 Pages
  • User Level: Science
  • Publication Date: August 13, 2012
  • Available eBook Formats: PDF
Full Description
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. These technologies face particular challenges due to the inherent multi-source nature of the information explosion.  The technologies must now handle not isolated texts or individual narratives, but rather large-scale repositories and streams---in general, in multiple languages---containing a multiplicity of perspectives, opinions, or commentaries on particular topics, entities or events.  There is thus a need to adapt existing techniques and develop new ones to deal with these challenges. This volume contains a selection of papers that present a variety of methodologies for content identification and extraction, as well as for content fusion and regeneration. The chapters cover various aspects of the challenges, depending on the nature of the information sought---names vs. events,--- and the nature of the sources---news streams vs. image captions vs. scientific research papers, etc. This volume aims to offer a broad and representative sample of studies from this very active research field.
Table of Contents

Table of Contents

  1. Part I Background and Fundamentals .
  2. 1.Automatic Text Summarization: Past, Present and Future. Horacio Saggion and Thierry Poibeau.
  3. Information Extraction: Past, Present and Future. Jakub Piskorski and Roman Yangarber.
  4. Part II Named Entity in a Multilingual Context.
  5. Learning to Match Names Across Languages. Inderjeet Mani, Alex Yeh, and Sherri Condon.
  6. Computational Methods for Name Normalization Using Hypocoristic Personal Name Variants. Patricia Driscoll.
  7. Entity Linking: Finding Extracted Entities in a Knowledge Base. Delip Rao, Paul McNamee, and Mark Dredze.
  8. A Study of the Effect of Document Representations in Clustering
  9. based Cross
  10. document Coreference Resolution. Horacio Saggion.
  11. Part III Information Extraction.
  12. Interactive Topic Graph Extraction and Exploration of Web Content. Günter Neumann and Sven Schmeier.
  13. Predicting Relevance of Event Extraction for the End User. Silja Huttunen, Arto Vihavainen, Mian Du, and Roman Yangarber.
  14. Open
  15. domain Multi
  16. Document Summarization via Information Extraction: Challenges and Prospects. Heng Ji, Benoit Favre, Wen
  17. Pin Lin, Dan Gillick, Dilek Hakkani
  18. Tur, and Ralph Grishman.
  19. Part IV Multi
  20. document Summarization.
  21. Generating Update Summaries: Using an Unsupervized Clustering Algorithm to Cluster Sentences. Aurélien Bossard.
  22. Multilingual Statistical News Summarization. Mijail Kabadjov, Josef Steinberger and Ralf Steinberger.
  23. A Bottom
  24. up Approach to Sentence Ordering for Multi
  25. document Summarization. Danushka Bollegala, Naoaki Okazaki, and Mitsuru Ishizuka.
  26. Improving Speech
  27. to
  28. Text Summarization by Using Additional Information Sources. Ricardo Ribeiro and David Martins de Matos.
  29. Multi
  30. Document Summarization Techniques for Generating Image Descriptions: A Comparative Analysis. Ahmet Aker, Laura Plaza, Elena Lloret, and Robert Gaizauskas.
  31. Index.​

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