Recent neural network approaches to summarization are largely either
selection-based extraction or generation-based abstraction. In this work, we
present a neural model for single-document summarization based on joint
extraction and syntactic compression. Our model chooses sentences from the
document, identifies possible compressions based on constituency parses, and
scores those compressions with a neural model to produce the final summary. For
learning, we construct oracle extractive-compressive summaries, then learn both
of our components jointly with this supervision. Experimental results on the
CNN/Daily Mail and New York Times datasets show that our model achieves strong
performance (comparable to state-of-the-art systems) as evaluated by ROUGE.
Moreover, our approach outperforms an off-the-shelf compression module, and
human and manual evaluation shows that our model's output generally remains
grammatical.

本文提出了一种基于联合提取和句法压缩的神经模型用于单文档摘要，该模型选择文档中的句子，通过句法分析识别可能的压缩，并用神经模型评分这些压缩以生成最终的摘要，实验结果表明，该模型在 ROUGE 评估中表现良好，能够达到与最先进系统相当的性能，并且其输出一般保持语法正确。