用摘要模型模拟简化文本，发现两者有较高重合度。
Can summarization approximate simplification? A gold standard comparison
- 用BART-based BRIO模型生成摘要
- 与人工标注简化文本对比，ROUGE-L达0.654
- 揭示摘要与简化在输出上的异同点
本研究探讨文本摘要与简化之间的重叠程度。尽管摘要评估方法已标准化，但简化任务缺乏统一标准。为此，我们采用两种基于BART的BRIO摘要方法处理Newsela语料库，将生成结果与人工标注的简化文本进行对比，最高ROUGE-L得分为0.654。该结果揭示了摘要与简化在输出上的相似与差异，为二者关系提供了实证参考。
原文摘要 · Abstract (English)
This study explores the overlap between text summarization and simplification outputs. While summarization evaluation methods are streamlined, simplification lacks cohesion, prompting the question: how closely can abstractive summarization resemble gold-standard simplification? We address this by applying two BART-based BRIO summarization methods to the Newsela corpus, comparing outputs with manually annotated simplifications and achieving a top ROUGE-L score of 0.654. This provides insight into where summarization and simplification outputs converge and differ.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。