Presentation 1999/12/20
AutomaticSpeechSummarizationBasedonWordSignificanceandLinguisticLikelihood
Chiori HORI, Sadaoki FURUI,
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Abstract(in English) This paper proposes a new method of automatically summarizjng speech by extracting a limited number of relatively important words from its automatic transcription according to a target compression ratio for the number of characters. To determine a word set to be extracted, we define a sumnlarization score consisting of a topic score(significance measure) of words and alinguistic score(likeihood) of the word concatenation. A set of words maximizing the score is efficiently selected using a dynamic programming(DP) technique. In order to evaluate the summarization scores of the summarized sentences obtained from the same original sentence using the various summarizing ratio, a normalization factor is applied to the summarization score.Japanese broadcast news speech transcribed using a large vocabulary continuous speech recognition system was summarized. As a result 86% of important words in the original speech were correctly included in the summarizing sentences and 72% of the smmarizing sentences could maintain the meanings of the original speech under the 60 70% summarization condition.
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Paper # NLC99-42
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Conference Information
Committee NLC
Conference Date 1999/12/20(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) AutomaticSpeechSummarizationBasedonWordSignificanceandLinguisticLikelihood
Sub Title (in English)
Keyword(1)
1st Author's Name Chiori HORI
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2nd Author's Name Sadaoki FURUI
2nd Author's Affiliation
Date 1999/12/20
Paper # NLC99-42
Volume (vol) vol.99
Number (no) 523
Page pp.pp.-
#Pages 6
Date of Issue