In this work, we model abstractive text summarization using Attentional
Encoder-Decoder Recurrent Neural Networks, and show that they achieve
state-of-the-art performance on two different corpora. We propose several novel
models that address critical problems in summarization that are not adequately
modeled by the basic architecture, such as modeling key-words, capturing the
hierarchy of sentence-to-word structure, and emitting words that are rare or
unseen at training time. Our work shows that many of our proposed models
contribute to further improvement in performance. We also propose a new dataset
consisting of multi-sentence summaries, and establish performance benchmarks
for further research.

该研究使用注意力编码 - 解码循环神经网络模型抽象化文本摘要，并在两个不同的语料库上展示其实现最先进的性能。同时，该研究还提出了几种解决文摘中的关键问题的新模型，比如建模关键词、捕捉句子到单词结构的层次以及提取训练时很少出现的单词等。此外，研究还建立了一个多句子的文摘数据集，以便后续的新研究能建立性能基准。