Advances in automatic speech summarization

This paper reports recent advances in automatic speech summarization method. In our proposed method, a set of words maximizing a summarization score is extracted from automatically transcribed speech. This extraction is performed according to a target compression ratio using a dynamic programming technique. The extracted set of words is then connected to build a summarized sentence. The summarization score consists of a word significance measure, a confidence measure, linguistic likelihood, and a word concatenation probability which is determined by a dependency structure in the original speech given by Stochastic Dependency Context Free Grammar (SDCFG). Japanese broadcast news speech transcribed using a large vocabulary continuous speech recognition (LVCSR) system is summarized using our proposed method and evaluated in comparison with manual summarization by human subjects. The manual summarization results are combined to build a word network, and word accuracy of each automatic summarization result is calculated comparing with the most similar word string in the network.