GENERATING VIDEO PRESENTATION FROM ARTICLES

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dc.contributor.authorPOKHREL, AAGAT
dc.contributor.authorAGARWAL, ANISH
dc.contributor.authorPURI, BIPIN
dc.contributor.authorPATHAK, BIRAJ BIKRAM
dc.date.accessioned2023-07-31T07:11:55Z
dc.date.available2023-07-31T07:11:55Z
dc.date.issued2023-05
dc.descriptionThis project proposes a method for generating video slide presentations from text articles. The proposed method involves text parsing, feature extraction, clustering, ranking, summarization, slide creation, speech synthesis, and video generation.en_US
dc.description.abstractThis project proposes a method for generating video slide presentations from text articles. The proposed method involves text parsing, feature extraction, clustering, ranking, summarization, slide creation, speech synthesis, and video generation. The BART model finetuned on dataset is used for feature extraction and abstractive summarization. The K Medoids clustering algorithm is used for clustering the sentence features, and the KNN algorithm is used for ranking. The markdown syntax and MARP are used for slide creation, and the Azure cognitive speech services and FFMPEG are used for speech synthesis and video generation, respectively. The comparision is drawn among BART large and BART base models on the CNN/DM dataset. The results show that the BART-large model outperforms in terms of ROUGE scores and employing the further pipeline generates coherent and informative video slide presentations.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14540/18853
dc.language.isoenen_US
dc.publisherI.O.E. Pulchowk Campusen_US
dc.subjectVideo slide presentation,en_US
dc.subjectText articles,en_US
dc.subjectKNN algorithmen_US
dc.titleGENERATING VIDEO PRESENTATION FROM ARTICLESen_US
dc.typeReporten_US
local.academic.levelBacheloren_US
local.affiliatedinstitute.titlePulchowk Campusen_US
local.institute.titleInstitute of Engineeringen_US
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