Event

Using Supervised and Unsupervised Approaches for Extractive Meeting Summarization

Shasha Xie

ICSI and University of Texas, Dallas

Thursday, March 05, 2009
2:30-3:30 p.m.

Meeting summarization provides a concise and informative summary for the lengthy meetings and is an effective tool for efficient information access. In this talk, the focus is extractive summarization, where salient sentences are selected from the meeting transcripts to form a summary. First, we exploited unsurpervised learning approach on the framework of MMR, and evaluated different measures to better capture the similarity between texts. Then, we adopted a supervised learning approach for this task and use a classifier to determine whether to select a sentence in the summary based on a rich set of features. We addressed three important problems associated with this supervised classification approach, imbalanced data problem, human annotation disagreement and the effectiveness of different features.