🤖 AI Summary
This work aims to enhance the quality and accuracy of abstractive text summarization on the English subset of the XL-Sum corpus. Building upon the Transformer-based PEGASUS model, we fine-tune it on the English XL-Sum data and evaluate its performance using ROUGE metrics. Experimental results demonstrate that our approach substantially outperforms the mT5 baseline, achieving relative improvements of 4.04%, 15.25%, and 3.39% in ROUGE-1, ROUGE-2, and ROUGE-L scores, respectively. To the best of our knowledge, this constitutes the state-of-the-art performance for abstractive summarization on this dataset.
📝 Abstract
Abstractive text summarization is the technique of generating a short and concise summary comprising the salient ideas of a source text without making a subset of the salient sentences from the source text. The introduction of transformer models such as BART, T5, and PEGASUS has made this sort of summarization process more efficient and accurate. The objective of this paper is to fine-tune PEGASUS on the XL-Sum English corpus to achieve a better performance compared to the baseline mT5 model. The performance of the generated summaries from the fine-tuned model is evaluated using the ROUGE metric, which basically compares the auto-generated summaries with human-created summaries. To the best of our knowledge, the results from our fine-tuned PEGASUS model give a state-of-the-art performance on the XL-Sum English Corpus. To quantify the improvement, there is a 4.04% improvement in the ROUGE-1 score, a 15.25% increase in the ROUGE-2 score, and a 3.39% improvement in the ROUGE-L score from the baseline model.