Abstract
We aim to extract automatically the summarization of spoken lectures for conferences and classes. For this purpose, at first we compared results of summarization extracted by human subjects. We found large differences with every subject. Then we investigated the relations between linguistic surface information and human results, and we obtained useful linguistic surface information. Next, we summarized spoken lectures on conferences and classes using the linguistic information. Additionally, we also focused on prosodic features; F0 and power. We conducted the same experiments on them. Lastly, we combined linguistic surface information and prosodic information. As a result, the proposed automatic summarization produced a better F- measure (0.599), k-value (0.420) and Rouge metric (0.758) comparable with human results.
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