Optimizing Context-Aware Summarization with Roberta and Structured Knowledge

In the age of information abundance, the need for effective summarization techniques that comprehend and preserve the nuanced context of textual data is critical. This research presents an optimized framework for Context-Aware Summarization using RoBERTa (Robustly Optimized BERT Approach) augmented with structured knowledge. The proposed methodology leverages RoBERTa’s advanced language understanding capabilities to generate rich contextual embeddings while incorporating domain-specific structured knowledge to enhance the informativeness and coherence of summaries. A meticulously constructed domain corpus, coupled with robust pre-processingtechniques, serves as the foundation for this approach. The methodology is evaluated using the CNN/DailyMail dataset, a benchmark for summarization tasks, with performance measured through standard metrics such as ROUGE. Results demonstrate the superiority of the proposed framework in capturing contextual depth and improving summary quality compared to conventional approaches. This study contributes to advancing summarization techniques by integrating robust language models and structured knowledge, paving the way for future developments in natural language processing and information retrieval systems.