GENERATING VIDEO PRESENTATION FROM ARTICLES

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.