Abstract 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 asMonologuei(which indicates a segment where speakeriis the dominant speaker, e.g., lecturing all the other participants) orDiscussionx1x2, . . .,xn(which indicates a segment where speakersx1toxninvolve 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.

In this paper, we aim to improve meeting summarization performance using discourse specific information. Since there are intrinsically different characteristics in utterances in different types of function segments, e.g., Monologue segments versus Discussion ones, we propose a new summarization framework where different summarizers are used for different segment types. For monologue segments, we adopt the integer linear programming-based summarization method; whereas for discussion segments, we use a graph-based method to incorporate speaker information. Performance of our proposed method is evaluated using the standard AMI meeting corpus. Results show a good improvement over previous state-of-the-art algorithms according to various evaluation metrics and different compress ratios.

Linear segmentation of a meeting conversation is beneficial as a stand-alone system (to organize a meeting and make it easier to access) or as a preprocessing step for many other meeting related tasks. Such segmentation can be done according to two different criteria: topic in which a meeting is segmented according to the different items in its agenda, and function in which the segmentation is done according to the meeting's different events (like discussion, monologue). In this article we concentrate on the function segmentation task and propose new unsupervised methods to segment a meeting into functionally coherent parts. The first proposed method assigns a score to each possible boundary according to its local information and then selects the best ones. The second method uses a dynamic programming approach to find the global best segmentation according to a defined cost function. Since these two methods are complementary of each other, we propose the third method as a combination of the first two ones, which takes advantage of both to improve the final segmentation. In order to evaluate our proposed methods, a subset of a standard meeting dataset (AMI) is manually annotated and used as the test set. Results show that our proposed methods perform significantly better than the previous unsupervised approach according to different evaluation metrics.