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

Hsiu-Wen Wang, Ding-Yuan Cheng, Chi-Hua Chen, Yu-Rou Wu, Chi-Chun Lo and Hui-Fei Lin
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Hsiu-Wen Wang: Department of Information Management and Finance, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan
Ding-Yuan Cheng: Department of Information Management, Hwa Hsia Institute of Technology, No. 111 Gongzhuan Road, Zhonghe District, New Taipei 235, Taiwan
Chi-Hua Chen: Department of Information Management and Finance, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan
Yu-Rou Wu: Department of Information Management and Finance, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan
Chi-Chun Lo: Department of Information Management and Finance, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan
Hui-Fei Lin: Department of Communication and Technology, National Chiao Tung University, No. 1001 University Road, Hsinchu 300, Taiwan

Sustainability, 2015, vol. 7, issue 4, 1-15

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.

Keywords: feature selection; information retrieval; speech summarization; text mining (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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