论文标题
精明简化：文本简化中的内容和解释生成
Elaborative Simplification: Content Addition and Explanation Generation in Text Simplification
论文作者
论文摘要
现代文本简化研究的大部分都集中在句子级的简化上，将原始的，更复杂的句子转换为简化版本。但是，当需要解释困难的概念和推理时，添加内容通常很有用。在这项工作中，我们介绍了文本简化中的第一个数据驱动的内容添加的数据驱动研究，我们称之为详尽的简化。我们介绍了新的注释数据集，其中包括新闻语料库中精制简化的1.3K实例，并分析了如何通过上下文特异性的角度详细说明实体，思想和概念。我们使用大规模的预训练语言模型建立了详细生成的基准，并证明在发电过程中考虑上下文特异性可以提高性能。我们的结果说明了精明简化的复杂性，这为将来的工作提出了许多有趣的方向。
Much of modern-day text simplification research focuses on sentence-level simplification, transforming original, more complex sentences into simplified versions. However, adding content can often be useful when difficult concepts and reasoning need to be explained. In this work, we present the first data-driven study of content addition in text simplification, which we call elaborative simplification. We introduce a new annotated dataset of 1.3K instances of elaborative simplification in the Newsela corpus, and analyze how entities, ideas, and concepts are elaborated through the lens of contextual specificity. We establish baselines for elaboration generation using large-scale pre-trained language models, and demonstrate that considering contextual specificity during generation can improve performance. Our results illustrate the complexities of elaborative simplification, suggesting many interesting directions for future work.
