A Hybrid Approach for Summarizing Text and Image Data Using ResNet and BART
摘要
With the increase in the volume and diversity of data available in various domains, the need for summarization techniques that can handle multiple data types has become crucial. Summarizing Heterogeneous data involves generating a concise representation of diverse data types, including text, image, audio, video, and structured data. In this paper, the focus is more on the major data types which are text, image, video, and text extracted from relational data. A multimodal architecture is created for processing and providing a summary in the text form. This involves a combined structure with several pre-trained models as well as Natural Language Processing-based libraries. Additionally, pre-trained models like BART and ResNet101 have been integrated.