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Hahn, U.: Automatisches Abstracting (2013)
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- Source
- Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried
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Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016)
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- Date
- 22. 1.2016 12:29:41
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Wang, S.; Koopman, R.: Embed first, then predict (2019)
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- Date
- 29. 9.2019 12:18:42
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Kannan, R.; Ghinea, G.; Swaminathan, S.: What do you wish to see? : A summarization system for movies based on user preferences (2015)
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- Date
- 25. 1.2016 18:45:29
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Galgani, F.; Compton, P.; Hoffmann, A.: Summarization based on bi-directional citation analysis (2015)
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- Source
- Information processing and management. 51(2015) no.1, S.1-24
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Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.1, S.71-82
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Kim, H.H.; Kim, Y.H.: ERP/MMR algorithm for classifying topic-relevant and topic-irrelevant visual shots of documentary videos (2019)
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- Abstract
- We propose and evaluate a video summarization method based on a topic relevance model, a maximal marginal relevance (MMR), and discriminant analysis to generate a semantically meaningful video skim. The topic relevance model uses event-related potential (ERP) components to describe the process of topic relevance judgment. More specifically, the topic relevance model indicates that N400 and P600, which have been successfully applied to the mismatch process of a stimulus and the discourse-internal reorganization and integration process of a stimulus, respectively, are used for the topic mismatch process of a topic-irrelevant video shot and the topic formation process of a topic-relevant video shot. To evaluate our proposed ERP/MMR-based method, we compared the video skims generated by the ERP/MMR-based, ERP-based, and shot boundary detection (SBD) methods with ground truth skims. The results showed that at a significance level of 0.05, the ROUGE-1 scores of the ERP/MMR method are statistically higher than those of the SBD method, and the diversity scores of the ERP/MMR method are statistically higher than those of the ERP method. This study suggested that the proposed method may be applied to the construction of a video skim without operational intervention, such as the insertion of a black screen between video shots.
