This research explores context-aware and multi-perspective summarization by integrating techniques from retrieval-augmented generation (RAG), large language models (LLMs), machine learning, and computer vision. It aims to enhance information retrieval and summarization across various applications, including event timeline generation, legal document analysis, cross-lingual case retrieval, and stance- aware news summarization. By leveraging intelligent retrieval mechanisms, the system ensures accurate, context-rich, and multi-viewpoint summaries. Its applications span legal, medical, and industrial specifications, enabling structured knowledge extraction from large, unstructured datasets while improving efficiency, interpretability, and decision-making support.