We describe a probabilistic approach to content selection 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 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
We describe a probabilistic approach to content selection 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 typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. 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