Blending Sentence Optimization Weights of Unsupervised Approaches for Extractive Speech Summarization

This paper evaluates the performance of two unsupervised approaches, Maximum Marginal Relevance (MMR) and concept-based global optimization framework for speech summarization. Automatic summarization is very useful techniques that can help the users browse a large amount of data. This study focuses on automatic extractive summarization on multi-dialogue speech corpus. We propose improved methods by blending each unsupervised approach at sentence level. Sentence level information is leveraged to improve the linguistic quality of selected summaries. First, these scores are used to filter sentences for concept extraction and concept weight computation. Second, we pre-select a subset of candidate summary sentences according to their sentence weights. Last, we extend the optimization function to a joint optimization of concept and sentence weights to cover both important concepts and sentences. Our experimental results show that these methods can improve the system performance comparing to the concept-based optimization baseline for both human transcripts and ASR output. The best scores are achieved by combining all three approaches, which are significantly better than the baseline system.