Multi-source, multilingual information extraction and summarization
著者
書誌事項
Multi-source, multilingual information extraction and summarization
（Theory and applications of natural language processing / series editors, Graeme Hirst, Eduard Hovy, Mark Johnson, Edited volumes）
Springer, c2013
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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. Gunter 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. Aurelien 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.
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