Paper: A Skip-Chain Conditional Random Field For Ranking Meeting Utterances By Importance

ACL ID W06-1643
Title A Skip-Chain Conditional Random Field For Ranking Meeting Utterances By Importance
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006
Authors

We describe a probabilistic approach to content se- lection for meeting summarization. We use skip- chain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as QUESTION-ANSWER that typi- cally appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss dif- ferent approaches for ranking all utterances in a se- quence using CRFs. Our best performing system achieves 91.3% of human performance when evalu- ated with the Pyramid evaluation metric, which rep- resents a 3.9% absolute increase compared to our most competitive non-sequential classifier.