Fine-Tuned PEGASUS Achieves State-of-the-Art Performance on XL-Sum English Corpus

This paper presents a method for optimizing abstractive text summarization by fine-tuning the PEGASUS model on the XL-Sum English corpus. The objective is to surpass the performance of the baseline mT5 model in generating concise summaries that capture salient ideas without merely extracting sentences. The generated summaries are evaluated using the ROUGE metric, which compares auto-generated outputs against human-created references. The study claims that the fine-tuned PEGASUS model achieves state-of-the-art results on this specific dataset. Quantitative analysis reveals a 4.04% improvement in the ROUGE-1 score compared to the baseline. Additionally, the model demonstrates a significant 15.25% increase in the ROUGE-2 score. Finally, there is a reported 3.39% improvement in the ROUGE-L score, confirming the effectiveness of the fine-tuning approach.