Linear Discourse Segmentation of Multi-Party Meetings Based on Local and Global Information

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.