This study provides an in-depth comparative analysis of 10 diverse and latest news summarization techniques, evaluating them across various dimensions to assess their efficiency and applicability. We assess each method based on methodology, innovation, ROUGE scores, limitations, and potential for future research. Through this comprehensive exploration, we illuminate the strengths and weaknesses of each approach, offering valuable insights for researchers and practitioners seeking to find an appropriate summarization method according to their requirements. Remarkably, the News Text Generation Method integrating pointer generator network stands out, it achieves the highest ROUGE-L score of 55.5. Following closely is the legal news summarization model, based on RoBERTa, T5, and Dilated Gated CNN, with the second-highest ROUGE-L score of 45.8. This comparative analysis serves as a pivotal resource for advancing the field of news summarization, highlighting the News Text Generation Method’s promising potential. The findings of this study contribute significantly to the ongoing evolution of news summarization techniques, paving the way for further advancements and innovations in this dynamic domain.