Exploring Speaker Characteristics for Meeting Summarization

In this paper, we investigate using meeting-specific characteris-tics to improve extractive meeting summarization, in particular, speaker-related attributes (such as verboseness, gender, native language, role in the meeting). A rich set of speaker-sensitive features are developed in the supervised learning framework. We perform experiments on the ICSI meeting corpus. Re-sults are evaluated using multiple criteria, including ROUGE, a sentence-level F-measure, and an approximated Pyramid ap-proach. We show that incorporating speaker characteristics can consistently improve summarization performance on various testing conditions. 1