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Multilingual Summarization: Dimensionality Reduction and a Step Towards Optimal Term Coverage

Published

Author(s)

Yi-Kai Liu, John M. Conroy, Sashka T. Davis, Jeff Kubina, Dianne P. O'Leary, Judith D. Schlesinger

Abstract

In this paper we present three term weighting approaches for multi-lingual document summarization and give results on the DUC 2002 data as well as on the 2013 Multilingual Wikipedia feature articles data set. We introduce a new interval-bounded nonnegative matrix factorization. We use this new method, latent semantic analysis (LSA), and latent Dirichlet allocation (LDA) to give three term- weighting methods for multi-document multi-lingual summarization. Results on DUC and TAC data, as well as on the MultiLing 2013 data, demonstrate that these methods are very promising, since they achieve oracle coverage scores in the range of humans for 6 of the 10 test languages.
Conference Dates
August 9, 2013
Conference Location
Sofia
Conference Title
MultiLing 2013

Keywords

document summarization, nonnegative matrix factorization

Citation

Liu, Y. , Conroy, J. , Davis, S. , Kubina, J. , O'Leary, D. and Schlesinger, J. (2013), Multilingual Summarization: Dimensionality Reduction and a Step Towards Optimal Term Coverage, MultiLing 2013, Sofia, -1, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=914103 (Accessed June 23, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created August 9, 2013, Updated February 19, 2017