Automatic Summarization

This paper proposes an automatic speech summarization technique for English. In our proposed method, a set of words maximizing a summarization score indicating appropriateness of summarization is extracted from automatically transcribed speech and concatenated to create a summary. The extraction process is performed using a Dynamic Programming (DP) technique according to a target compression ratio. In this paper, English broadcast news speech transcribed using a speech recognizer is automatically summarized. In order to apply our method, originally proposed for Japanese, to English, the model of estimating word concatenation probabilities based on a dependency structure in the original speech given by a Stochastic Dependency Context Free Grammar (SDCFG) is modified. A summarization method for multiple utterances using twolevel DP technique is also proposed. The automatically summarized sentences are evaluated by a summarization accuracy based on the comparison with the manual summarization of correctly transcribed speech by human subjects. Experimental results show that our proposed method effectively extracts relatively important information and remove redundant and irrelevant information from English news speech.

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