Sample Efficient Multimodal Semantic Augmentation for Incremental Summarization

Our work utilizes Summarization is the consolidated format for a learning of these semantic concepts as an intermediate large document and has been widely used for step from the videos. These semantic concepts many applications i.e., understanding a long meeting/event, along with the transcriptions (semantic augmentation) story summarization etc. Abstractive as input to a pre-trained summarizer model summarization is challenging in the Natural Language enrich the performance. In this work, we address Generation(NLG) domain as it requires an the problem of (i) generating semantically relevant understanding of all the salient information in annotations of a video (semantic concepts) using a the input document and rewriting logically in a fixed number of sampled frames from each video condensed manner rather than selection (extractive).