论文标题
简化文本的调查
A Survey on Text Simplification
论文作者
论文摘要
文本简化（TS）旨在降低内容的语言复杂性，以使其更容易理解。 TS的研究引起了人们的兴趣，尤其是随着TS的方法已从手动，手工制作的规则转变为自动化的简化。这项调查旨在提供TS的全面概述，包括对所使用的早期方法的简要描述，简化的各个方面（词汇，语义和句法）的讨论，以及该领域中使用的最新技术。我们注意到，该领域的研究显然已转向利用深度学习技术来执行TS，具体的重点是开发解决方案，以打击缺乏可用于简化的数据。我们还包括对通常使用的数据集和评估指标的讨论，以及对自然语言处理（NLP）中相关领域（例如语义相似性）的讨论。
Text Simplification (TS) aims to reduce the linguistic complexity of content to make it easier to understand. Research in TS has been of keen interest, especially as approaches to TS have shifted from manual, hand-crafted rules to automated simplification. This survey seeks to provide a comprehensive overview of TS, including a brief description of earlier approaches used, discussion of various aspects of simplification (lexical, semantic and syntactic), and latest techniques being utilized in the field. We note that the research in the field has clearly shifted towards utilizing deep learning techniques to perform TS, with a specific focus on developing solutions to combat the lack of data available for simplification. We also include a discussion of datasets and evaluations metrics commonly used, along with discussion of related fields within Natural Language Processing (NLP), like semantic similarity.
