Extracting question/answer pairs in multi-party meetings

Understanding multi-party meetings involves tasks such as dialog act segmentation and tagging, action item extraction, and summarization. In this paper we introduce a new task for multi-party meetings: extracting question/answer pairs. This is a practical application for further processing such as summarization. We propose a method based on discriminative classification of individual sentences as questions and answers via lexical, speaker, and dialog act tag information, followed by a contextual optimization via Markov models. Our results indicate that it is possible to outperform a non-trivial baseline using dialog act tag information. More specifically, our method achieves a 13% relative improvement over the baseline for the task of detecting answers in meetings.