The first part introduces an approach to synthesizing long video textures from short clips by rearranging segments coherently, while also considering audio conditioning. The second part discusses a novel technique for generating concise visual summaries of lengthy videos guided by natural language cues. Additionally, we focus specifically on summarizing instructional videos, capitalizing on audio-visual alignments and task structures to produce informative summaries. To further enrich the comprehension of instructional videos, the thesis introduces a cutting-edge approach that facilitates the learning and verification of procedural steps within instructional content, empowering the model to grasp long and complex video sequences and ensure procedural accuracy. Lastly, the potential of large language models is explored for answering questions about images through code generation. Through comprehensive experiments, the research demonstrates the efficacy of the proposed methodologies, envisioning promising future prospects in the field of semantics in long videos by integrating audio, visual, and textual relationships.