Journal of the Korean Society for Library and Information Science
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- P-ISSN1225-598X
- E-ISSN2982-6292
Investigating an Automatic Method in Summarizing a Video Speech Using User-Assigned Tags
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Abstract
We investigated how useful video tags were in summarizing video speech and how valuable positional information was for speech summarization. Furthermore, we examined the similarity among sentences selected for a speech summary to reduce its redundancy. Based on such analysis results, we then designed and evaluated a method for automatically summarizing speech transcripts using a modified Maximum Marginal Relevance model. This model did not only reduce redundancy but it also enabled the use of social tags, title words, and sentence positional information. Finally, we compared the proposed method to the Extractor system in which key sentences of a video speech were chosen using the frequency and location information of speech content words. Results showed that the precision and recall rates of the proposed method were higher than those of the Extractor system, although there was no significant difference in the recall rates.
- keywords
- MMR Model, Social Summarization, Redundancy, Acoustic features, Prosodic Features, Extractor, Transcripts, 스피치 요약, 태그, 비디오, 표제, 코싸인 유사계수, 내재적 평가, 적합 문장
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