We present a new neural model for text summarization that first extracts
sentences from a document and then compresses them. The proposed model offers a
balance that sidesteps the difficulties in abstractive methods while generating
more concise summaries than extractive methods. In addition, our model
dynamically determines the length of the output summary based on the gold
summaries it observes during training and does not require length constraints
typical to extractive summarization. The model achieves state-of-the-art
results on the CNN/DailyMail and Newsroom datasets, improving over current
extractive and abstractive methods. Human evaluations demonstrate that our
model generates concise and informative summaries. We also make available a new
dataset of oracle compressive summaries derived automatically from the
CNN/DailyMail reference summaries.

提出了一种新的神经模型用于文本摘要，首先从文档中提取句子然后再压缩它们，达到了抽象方法所暴露出的难度和提取方法所缺乏的简洁性的平衡，且所提模型在 CNN/DailyMail 和 Newsroom 数据集上均取得了最先进的效果。