The enormous information overload caused by the exponential growth of digital content across businesses is making it more challenging to adopt effective automatic summarizing solutions. Abstractive text summarizing has grown to be an important area in NLP with the aim of creating concise, clear, and instructive summaries that go beyond basic source text extraction. Summarization systems continue to struggle with issues of semantic purity, factual correctness, and contextual alignment despite tremendous advancements made possible by deep learning and transformer-based architectures. The study uses the BART model, which has been refined on a sizable news dataset, to examine abstractive summarization with an emphasis on semantic and contextual alignment. The suggested system incorporates contextual alignment techniques and semantic-aware preprocessing to guarantee that generated summaries maintain coherence and fluency while preserving meaning fidelity. The main evaluation metric used in the study is ROUGE, which is supplemented by knowledge from more modern semantic similarity-based methodologies. When compared to baseline summarization systems, experimental results show significant gains in content selection, semantic consistency, and textual coherence, especially for longer inputs. The evaluation further highlights the benefits of incorporating semantic and contextual representations into training pipelines, showing that such improvements can reduce factual inconsistencies and improve the total informativeness of summaries that were produced.