A skip-chain conditional random field for ranking meeting utterances by importance

  • Authors:
  • Michel Galley

  • Affiliations:
  • Columbia University, New York, NY

  • Venue:
  • EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as Question-Answer that typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss different approaches for ranking all utterances in a sequence using CRFs. Our best performing system achieves 91.3% of human performance when evaluated with the Pyramid evaluation metric, which represents a 3.9% absolute increase compared to our most competitive non-sequential classifier.