论文标题
关于神经抽象摘要方法和摘要的事实一致性的调查
A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization
论文作者
论文摘要
自动汇总是在计算上缩短一组文本数据的过程，创建一个代表原始文本中最重要信息片段的子集（摘要）。现有的摘要方法可以大致分为两种类型：提取和抽象。从源文档中，提取性摘要明确选择文本片段（单词，短语，句子等），而抽象摘要器会生成新颖的文本片段，以传达源中普遍存在的最显着概念。
Automatic summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) that represents the most important pieces of information in the original text. Existing summarization methods can be roughly divided into two types: extractive and abstractive. An extractive summarizer explicitly selects text snippets (words, phrases, sentences, etc.) from the source document, while an abstractive summarizer generates novel text snippets to convey the most salient concepts prevalent in the source.
