Automatic sentence segmentation of speech for automatic summarization

This paper presents an automatic sentence segmentation method for an automatic speech summarization system. The segmentation method is based on combining word- and class-based statistical language models to predict sentence and non-sentence boundaries. We study both the performance of the sentence segmentation system itself and the effect of the segmentation on the summarization accuracy. The sentence segmentation is done by modelling the probability of a sentence boundary given a certain word history with language models trained on transcriptions and texts from several sources. The resulting segmented data is used as the input to an existing automatic summarization system to determine the effect it has on the summarization process. We conduct all our experiments with two types of evaluation data: broadcast news and lecture transcriptions. The automatic summarizations are created with different sentence segmentations and different summarization ratios (30% and 40%) and evaluated by comparing them to human-made summaries. We show that a proper sentence segmentation is essential to achieve good performance with an automatic summarization system.