Michel Galley

Identifying Agreement and Disagreement for Summarization of Conversational Speech

Abstract:

In this talk, I will describe a statistical approach for modeling agreements and disagreements in conversational interaction using Bayesian networks. This approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational and structural features that look both forward and backward in the discourse. It then classifies utterances as either agreement, disagreement, backchannel, or other using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. I will also discuss the use of detected agreements and disagreements in meetings to perform automatic summarization of meeting transcripts.