We present a method to produce abstractive summaries of long documents that
exceed several thousand words via neural abstractive summarization. We perform
a simple extractive step before generating a summary, which is then used to
condition the transformer language model on relevant information before being
tasked with generating a summary. We show that this extractive step
significantly improves summarization results. We also show that this approach
produces more abstractive summaries compared to prior work that employs a copy
mechanism while still achieving higher rouge scores. Note: The abstract above
was not written by the authors, it was generated by one of the models presented
in this paper.

本文采用神经抽象总结的方法来产生长文档的抽象总结，该方法使用一个简单的抽取步骤来生成一个摘要，然后将其用作相关信息的转换器语言模型的条件，然后生成一个摘要。我们展示了这个抽取步骤显著地改善了总结结果，而且这种方法产生的抽象总结比以前采用复制机制的工作能够实现更高的浸润得分。