Multi-modal feature integration for story boundary detection in broadcast news

This paper investigates how to integrate multi-modal features for story boundary detection in broadcast news. The detection problem is formulated as a classification task, i.e., classifying each candidate into boundary/non-boundary based on a set of features. We use a diverse collection of features from text, audio and video modalities: lexical features capturing the semantic shifts of news topics and audio/video features reflecting the editorial rules of broadcast news. We perform a comprehensive evaluation on boundary detection performance for six popular classifiers, including decision tree (DT), Bayesian network (BN), naive Bayesian (NB) classifier, multi-layer peceptron (MLP), support vector machines (SVM) and maximum entropy (ME) classifier. Results show that BN and DT can generally achieve superior performances over other classifiers and BN offers the best F1-measure. Analysis of BN and DT reveals important inter-feature dependencies and complementarities that contribute significantly to the performance gain.