微调PEGASUS模型，显著提升英文摘要生成效果。
Optimizing Abstractive Summarization With Fine-Tuned PEGASUS
- 在XL-Sum数据集上微调PEGASUS模型，优化摘要生成。
- 相比基线mT5模型，ROUGE-1提升4.04%，ROUGE-2提升15.25%。
- 适合关注文本摘要性能优化的研究者与应用开发者。
抽取式摘要通过从原文中选取关键句子生成简短摘要，而抽象式摘要则需生成包含核心思想的新语句。Transformer模型如BART、T5和PEGASUS显著提升了该任务的效率与准确性。本文旨在对PEGASUS模型在XL-Sum英文语料库上进行微调，以超越基线mT5模型的表现。使用ROUGE指标评估生成摘要与人工摘要的相似度。据我们所知，微调后的PEGASUS模型在XL-Sum英文数据集上达到当前最优性能：相比基线模型，ROUGE-1得分提升4.04%，ROUGE-2得分提升15.25%，ROUGE-L得分提升3.39%。
原文摘要 · Abstract (English)
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.
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