Visual evaluation of text features for document summarization and analysis

Daniela Oelke, Peter Bak, Daniel A. Keim, Mark Last, Guy Danon

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

Thanks to the web-related and other advanced technologies, textual information is increasingly being stored in digital form and posted online. Automatic methods to analyze such textual information are becoming inevitable. Many of those methods are based on quantitative text features. Analysts face the challenge to choose the most appropriate features for their tasks. This requires effective approaches for evaluation and feature-engineering. In this paper we suggest an approach to visually evaluate text-analysis features as part of an interactive feedback loop between evaluation and feature engineering. We apply document-fingerprinting for visualizing text features as an integral part of the analytic process. Consequently, analysts are able to access interim results of the applied automatic methods and alter their properties to achieve better results. We implement and evaluate the methodology on two different tasks, namely opinion analysis and document summarization and show that our iterative method leads to improved performance.

Original languageEnglish
Title of host publicationVAST'08 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings
PublisherIEEE Computer Society
Pages75-82
Number of pages8
ISBN (Print)9781424429356
DOIs
StatePublished - 1 Jan 2008
EventIEEE Symposium on Visual Analytics Science and Technology, VAST'08 - Columbus, OH, United States
Duration: 21 Oct 200823 Oct 2008

Publication series

NameVAST'08 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings

Conference

ConferenceIEEE Symposium on Visual Analytics Science and Technology, VAST'08
Country/TerritoryUnited States
CityColumbus, OH
Period21/10/0823/10/08

Keywords

  • I.5.2 [pattern recognition]: Design methodology - Feature evaluation and selection
  • I.7.5 [document and text processing]: Document capture - Document analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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