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Educational Videos subtitles’summarization Using Latent Dirichlet Allocation and Length Enhancement

Computers, Materials & Continua 2022年第3期70卷 6205-6221页

作者： Sarah S.Alrumiah Amal A.Al-Shargabi Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia

Nowadays,people use online resources such as educational videos and ***,such videos and courses are mostly long and thus,summarizing them will be *** video contents(visual,audio,and subtitles)could be analyzed to generate textual summaries,i.e.,***’subtitles contain significant ***,summarizing subtitles is effective to concentrate on the necessary *** of the existing studies used Term Frequency-Inverse Document Frequency(TF-IDF)and Latent Semantic Analysis(LSA)models to create lectures’*** study takes another approach and applies LatentDirichlet Allocation(LDA),which proved its effectiveness in document ***,the proposed LDA summarization model follows three *** first phase aims to prepare the subtitle file for modelling by performing some preprocessing steps,such as removing stop *** the second phase,the LDA model is trained on subtitles to generate the keywords list used to extract important *** in the third phase,a summary is generated based on the keywords *** generated summaries by LDA were lengthy;thus,a length enhancement method has been *** the evaluation,the authors developed manual summaries of the existing“EDUVSUM”educational videos *** authors compared the generated summaries with the manual-generated outlines using two methods,(i)Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and(ii)human *** performance of LDA-based generated summaries outperforms the summaries generated by TF-IDF and *** reducing the summaries’length,the proposed length enhancement method did improve the summaries’precision *** domains,such as news videos,can apply the proposed method for video summarization.

关键词： subtitle summarization educational videos topic modelling LDA extractive summarization

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