GENERATING VIDEO PRESENTATION FROM ARTICLES
Date
2023-05
Journal Title
Journal ISSN
Volume Title
Publisher
I.O.E. Pulchowk Campus
Abstract
This 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.
Description
This 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.
Keywords
Video slide presentation,, Text articles,, KNN algorithm