Exploring speaker characteristics for meeting summarization

In this paper, we investigate using meeting-specific characteristics 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. Results are evaluated using multiple criteria, including ROUGE, a sentence-level F-measure, and an approximated Pyramid approach. We show that incorporating speaker characteristics can consistently improve summarization performance on various testing conditions.

• Type: Journal
• Year: 2010
• Where: INTERSPEECH
• Authors: Fei Liu, Yang Liu
• Keywords: Extractive Meeting Summarization, ICSI Meeting Corpus, INTERSPEECH 2010, Signal Processing, Supervised Learning Framework