An interactive system for finding complementary literatures: a stimulus to scientific discovery
Artificial Intelligence - Special issue on scientific discovery
PERSIVAL, a system for personalized search and summarization over multimedia healthcare information
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Language and Learning for Robots
Language and Learning for Robots
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
A comparison of rankings produced by summarization evaluation measures
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
The automatic creation of literature abstracts
IBM Journal of Research and Development
Hi-index | 0.00 |
Text summarizers automatically construct summaries of a natural-language document. This paper examines the use of text summarization within data mining, identifying the potential summarizers have for uncovering interesting and unexpected information. It describes the current state of the art in commercial summarization and current approaches to the evaluation of summarizers. The paper then proposes a new model for text summarization and suggests a new form of evaluation. It argues that for summaries to be truly useful within data mining, they must include concepts abstracted from the text in addition to sentences extracted from the text. The paper uses two news articles to illustrate its points.