Paper Info
Title
Visual Evaluation Of Text Features For Document Summarization And Analysis
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.
Year
DOI
Venue
2008
10.1109/VAST.2008.4677359
IEEE SYMPOSIUM ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY 2008, PROCEEDINGS
Keywords
Field
DocType
1.7.5 [Document and Text Processing]: Document Capture-Document Analysis, 1.5.2 [Pattern Recognition]: Design Methodology-Feature evaluation and selection
Data mining,Data visualization,Text mining,Information retrieval,Computer science,Visualization,Feature extraction,Document summarization,Pixel,Statistical classification,Benchmark (computing)
Conference
ISSN
Citations 
PageRank 
2325-9442
9
0.76
References 
Authors
16
5
Name
Order
Citations
PageRank
Daniela Oelke122513.18
Peter Bak227618.09
Daniel A. Keim377041141.60
M. Last4767.05
Guy Danon5101.13