Presentation | 1999/12/20 AutomaticSpeechSummarizationBasedonWordSignificanceandLinguisticLikelihood Chiori HORI, Sadaoki FURUI, |
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Abstract(in Japanese) | (See Japanese page) |
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. |
Keyword(in Japanese) | (See Japanese page) |
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Paper # | NLC99-42 |
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Committee | NLC |
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Conference Date | 1999/12/20(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Natural Language Understanding and Models of Communication (NLC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | AutomaticSpeechSummarizationBasedonWordSignificanceandLinguisticLikelihood |
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1st Author's Name | Chiori HORI |
1st Author's Affiliation | () |
2nd Author's Name | Sadaoki FURUI |
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Date | 1999/12/20 |
Paper # | NLC99-42 |
Volume (vol) | vol.99 |
Number (no) | 523 |
Page | pp.pp.- |
#Pages | 6 |
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