This paper presents automatic speech summarization techniques and its evaluation metrics, focusing on sentence extraction-based summarization methods for making abstracts from spontaneous presentations. Since humans tend to summarize presentations by extracting important sentences from introduction and conclusion parts, this paper proposes a method using sentence location. Experimental results show that the proposed method significantly improves automatic speech summarization performance for the condition of 10% summarization ratio. Results of correlation analysis between subjective and objective evaluation scores confirm that objective evaluation metrics, including summarization accuracy, sentence F-measure and ROUGE-N, are effective for evaluating summarization techniques.

This paper presents techniques for speech-to-text and speech-to-speech automatic summarization based on speech unit extraction and concatenation. For the former case, a two-stage summarization method consisting of important sentence extraction and word-based sentence compaction is investigated. Sentence and word units which maximize the weighted sum of linguistic likelihood, amount of information, confidence measure, and grammatical likelihood of concatenated units are extracted from the speech recognition results and concatenated for producing summaries. For the latter case, sentences, words, and between-filler units are investigated as units to be extracted from original speech. These methods are applied to the summarization of unrestricted-domain spontaneous presentations and evaluated by objective and subjective measures. It was confirmed that proposed methods are effective in spontaneous speech summarization.