Enhancing Text Summarization Techniques: Comparative Study of BART and T5 Models

With the ever-increasing volumes of unstructured data and the trend toward fewer words and higher understandability, efficient text summarization is called for. In this paper, the authors compare the performance of two leading models, T5 and BART, on document summarization tasks using CNN and Super Glue datasets. This work gives an exhaustive evaluation of the strengths and weaknesses of both models with respect to the BLEU, METEOR, and ROUGE metrics. The results showed that BART is particularly suited to structured and accurate summarization tasks, thus achieving better BLEU and ROUGE scores in the CNN dataset. However, T5 performs exceptionally well in flexibility and semantic understanding, just as was observed in the Super Glue dataset, more specifically in METEOR scores.