Paper: Bayesian Learning In Text Summarization

ACL ID H05-1032
Title Bayesian Learning In Text Summarization
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors
  • Tadashi Nomoto (National Institute of Japanese Literature, Tokyo Japan)

The paper presents a Bayesian model for text summarization, which explicitly en- codes and exploits information on how hu- man 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 lever- ages performance of a summarizer, at times giving it a significant lead over non- Bayesian models.