Speech summarization: An approach through word extraction and a method for evaluation
In this paper, we propose a new method of automatic speech summarization for each utterance, where a set of words that maximizes a summarization score is extracted from automatic speech transcriptions. The summarization score indicates the appropriateness of summarized sentences. This extraction is achieved by using a dynamic programming technique according to a target summarization ratio. This ratio is the number of characters/words in the summarized sentence divided by the number of characters/words in the original sentence. The extracted set of words is then connected to build a summarized sentence. The summarization score consists of a word significance measure, linguistic likelihood, and a confidence measure. This paper also proposes a new method of measuring summarization accuracy based on a word network. expressing manual summarization results. The summarization accuracy of each automatic summarization is calculated by comparing it with the most similar word string in the network. Japanese broadcast-news speech, transcribed using a large-vocabulary continuous-speech recognition (LVCSR) system, is summarized and evaluated using our proposed method with 20, 40, 60, 70 and 80% summarization ratios. Experimental results reveal that the proposed method can effectively extract relatively important information by removing redundant or irrelevant information.
speech summarization summarization score dynamic programming summarization accuracy sentence compaction word network of manual summarization result