This research work provides a concise comparative analysis of prominent content summarization models, namely Textrank, T5, Pegasus, and Bart. The models are evaluated for their specific strengths: Textrank excels in sentence extraction, T5 showcases versatility, Pegasus demonstrates superior abstractive summarization, and Bart proves robust across diverse content types. Evaluation metrics, including ROUGE scores and human assessment, offer insights into each model’s performance. Beyond performance, the paper considers computational efficiency. The study contributes to content summarization by incorporating machine learning and LSTM techniques, advancing automated text processing. This concise reference aids professionals in selecting optimal summarization models for specific use cases and resource constraints. Evaluation metrics, including ROUGE scores, highlight Pegasus as the top performer out of the implemented models. Pegasus demonstrates exceptional capability in generating summaries aligned with references.