This paper explores the intersection of deep learning and natural language processing for semantic compression. We propose a novel architecture that combines transformer-based encoders with specialized decoders optimized for information preservation. Our experiments on multiple datasets demonstrate that our approach achieves state-of-the-art compression ratios while maintaining semantic fidelity. We evaluate our method using both automated metrics and human evaluations, showing significant improvements over baseline methods. The results suggest that our approach can be effectively applied to various domains including scientific literature, legal documents, and conversational data.