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Sustainability 2015, 7(4), 3885-3899; doi:10.3390/su7043885

A Novel Real-Time Speech Summarizer System for the Learning of Sustainability

1
Department of Information Management and Finance, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan
2
Department of Information Management, Hwa Hsia Institute of Technology, No. 111 Gongzhuan Road, Zhonghe District, New Taipei 235, Taiwan
3
Telecommunication Laboratories, Chunghwa Telecom Co., Ltd., No. 99, Dianyan Road, Yangmei City, Taoyuan County 326, Taiwan
4
Department of Communication and Technology, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Received: 13 January 2015 / Revised: 20 March 2015 / Accepted: 30 March 2015 / Published: 2 April 2015
(This article belongs to the Section Sustainable Education and Approaches)
View Full-Text   |   Download PDF [1173 KB, uploaded 2 April 2015]   |  

Abstract

As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures). Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS) was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words) and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability. View Full-Text
Keywords: feature selection; information retrieval; speech summarization; text mining feature selection; information retrieval; speech summarization; text mining
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Wang, H.-W.; Cheng, D.-Y.; Chen, C.-H.; Wu, Y.-R.; Lo, C.-C.; Lin, H.-F. A Novel Real-Time Speech Summarizer System for the Learning of Sustainability. Sustainability 2015, 7, 3885-3899.

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