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. Japanese broadcast news speech transcribed using a large vocabulary continuous speech recognition system is summarized 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.