Jacob Eisenstein, Regina Barzilay, Randall Davis
Creating video recordings of events such as lectures or meetings is increasingly inexpensive and easy. However, reviewing the content of such video may be time-consuming and difficult. Our goal is to produce a "comic book" summary, in which a transcript is augmented with keyframes that disambiguate and clarify accompanying text. Unlike most previous keyframe extraction systems which rely primarily on visual cues, we present a linguistically-motivated approach that selects keyframes that contain salient gestures. Rather than learning gesture salience directly, it is estimated by measuring the contribution of gesture to understanding other discourse phenomena. More specifically, we bootstrap from multimodal coreference resolution to identify gestures that improve performance. We then select keyframes that capture these gestures. Our model predicts gesture salience as a hidden variable in a conditional framework, with observable features from both the visual and textual modalities. This approach significantly outperforms competitive baselines that do not use gesture information.
Subjects: 13.1 Discourse; 13. Natural Language Processing
Submitted: Apr 24, 2007