Exploiting structural meeting-specific features for topic segmentation
1552Authors
nan
Abstract
AbstractIn this article we address the task of automatic text structuring into linear and non-overlapping thematic episodes. Our investigation reports on the use of various lexical, acoustic and syntactic features, and makes a comparison of how these features influence performance of automatic topic segmentation. Using datasets containing multi-party meeting transcriptions, we base our experiments on a proven state-of-the-art approach using support vector classification.
Keywords
Full Content
nan
Labeled Text
AbstractIn this article we address the task of automatic text structuring into linear and non-overlapping thematic episodes. Our investigation reports on the use of various lexical, acoustic and syntactic features, and makes a comparison of how these features influence performance of automatic topic segmentation. Using datasets containing multi-party meeting transcriptions, we base our experiments on a proven state-of-the-art approach using support vector classification.
Publication Details
- Paper ID:
- 1552
- Year:
- 2007
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan