Toward Automatic Generation of Image-Text Document Surrogates to Optimize Cognition Conference Paper

abstract

• The representation of information collections needs to be optimized for human cognition. Growing information collections play a crucial role in human experiences. While documents often include rich visual components, collections, including personal collections and those generated by search engines, are typically represented lists of text-only surrogates. By concurrently invoking complementary components of human cognition, combined image-text surrogates help people to more effectively see, understand, think about, and remember information collection. This research develops algorithmic methods that use the structural context of images in HTML documents to associate meaningful text and thus derive combined image-text surrogates.

name of conference

• Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries

authors

• Kerne, Andruid

published proceedings

• JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES

author list (cited authors)

• Koh, E., Kerne, A., & Moeller, J.

citation count

• 1

complete list of authors

• Koh, Eunyee||Kerne, Andruid||Moeller, Jon

editor list (cited editors)

• Heath, F., Rice-Lively, M. L., & Furuta, R.

publication date

• January 2009

publisher

• Association for Computing Machinery (ACM)  Publisher

published in

• n2575-7865ISSN  Journal

keywords

• Information Extraction
• Search Representation
• Surrogates

Digital Object Identifier (DOI)

• 10.1145/1555400.1555491

International Standard Book Number (ISBN) 13

• 978-1-60558-697-7

start page

• 417

end page

• 417

URL

• https://doi.org/10.1145/1555400