Abstract
The paper presents a Bayesian model for text summarization, which explicitly encodes and exploits information on how human judgments are distributed over the text. Comparison is made against non Bayesian summarizers, using test data from Japanese news texts. It is found that the Bayesian approach generally leverages performance of a summarizer, at times giving it a significant lead over non- Bayesian models. © 2005 Association for Computational Linguistics.
Cite
CITATION STYLE
Nomoto, T. (2005). Bayesian learning in text summarization. In HLT/EMNLP 2005 - Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 249–256). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220575.1220607
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