Automatic text summarization plays an important role in transforming lengthy documents into concise and informative representations. The abstractive summarization aims to generate sentences that capture the semantic meaning of the source text. This study proposed semantic and syntactic augmentation in transformer-based abstractive summarization using two encoder–decoder models such as a BART-based model enhanced with SpaCy Part-of-Speech analysis and RoBERTa-based semantic embeddings, and a prompt-guided T5 model, in which task-specific textual prompts were appended to the input sequence to guide the summarization process during fine-tuning and inference. Both models were trained and evaluated on the BBC News dataset using ROUGE parameters. The final results show that the T5 model obtained ROUGE-1 of 45.61, ROUGE-2 of 30.12, and ROUGE-L of 45.12. The BART model obtained ROUGE-1 of 44.28, ROUGE-2 of 28.70, and ROUGE-L of 44.72. The results indicate that the prompt-based T5 model combined with linguistic feature augmentation can improve the quality of abstractive summaries. However, as the reference summaries in news datasets contain extractive characteristics, ROUGE-based evaluation reflects lexical overlap in addition to abstractive generation. The final findings show the improved abstractive summarization performance within the constraints of the dataset and evaluation protocol.