This paper describes a system that automatically generates meeting minutes by extracting a topic hierarchy from a meeting's speech. The topic hierarchy is a tree structure whose nodes comprise a topic summary. The topic structure extraction process converts speech recognition results into a word conceptual vector sequence and divides the sequence into the topic segments (topic segmentation). It classifies the topic segments hierarchically (segment clustering). Experimental results show that for the transcription of a meeting, the proposed algorithm is useful. Experiments on the transcription of a televised debate showed that the proposed topic segmentation algorithm is superior to the conventional method using local word frequency vectors. We also discuss experiments on the speech recognition results for the televised debate.