Modeling (in)variability of human judgments for text summarization

Authors:
Tadashi Nomoto;Yuji Matsumoto
Affiliations:
National Institute of Japanese Literature, Tokyo, Japan;Nara Institute of Science and Technology, Nara, Japan
Venue:
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Year:
2002

Citing 2
Cited 0

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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
SSDT: A Scalable Subspace-Splitting Classifier for Biased Data

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining

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Abstract

The paper proposes and empirically motivates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In particular, we explore the use of probabilistic decision tree within the clustering framework to account for the variation as well as regularity in human created summaries.