This paper presents a data-driven study focusing on analyzing and predicting sentence deletion --- a prevalent but understudied phenomenon in document simplification --- on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.

本文基于一个大型英文文本简化语料库，对一种常见但鲜为人知的现象——句子删除进行了数据驱动的研究和预测，检查了与句子删除相关的各种文档和语篇因素，揭示了专业编辑为满足小学和初中教育标准而采用的不同策略，并利用自动化对齐数据训练了分类模型以预测句子被删除的可能性。研究发现语篇层面的因素为句子简化中的句子删除预测带来了挑战。

文本简化中句子删除的话语层面因素