Extractive summarization of multi-party meetings through discourse segmentation

In this article we tackle the problem of multi-party conversation summarization. We investigate the role of discourse segmentation of a conversation on meeting summarization. First, an unsupervised function segmentation algorithm is proposed to segment the transcript into functionally coherent parts, such as Monologue i (which indicates a segment where speaker i is the dominant speaker, e.g., lecturing all the other participants) or Discussion x 1 x 2 , . . ., x n (which indicates a segment where speakers x 1 to x n involve in a discussion). Then the salience score for a sentence is computed by leveraging the score of the segment containing the sentence. Performance of our proposed segmentation and summarization algorithms is evaluated using the AMI meeting corpus. We show better summarization performance over other state-of-the-art algorithms according to different metrics.