Paper: Supervised Ranking In Open-Domain Text Summarization

ACL ID P02-1059
Title Supervised Ranking In Open-Domain Text Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2002
Authors

The paper proposes and empirically moti- vates an integration of supervised learning with unsupervised learning to deal with human biases in summarization. In par- ticular, we explore the use of probabilistic decision tree within the clustering frame- work to account for the variation as well as regularity in human created summaries. The corpus of human created extracts is created from a newspaper corpus and used as a test set. We build probabilistic de- cision trees of different flavors and in- tegrate each of them with the clustering framework. Experiments with the cor- pus demonstrate that the mixture of the two paradigms generally gives a signif- icant boost in performance compared to cases where either of the two is considered alone.