In the past few years, neural abstractive text summarization with
sequence-to-sequence (seq2seq) models have gained a lot of popularity. Many
interesting techniques have been proposed to improve seq2seq models, making
them capable of handling different challenges, such as saliency, fluency and
human readability, and generate high-quality summaries. Generally speaking,
most of these techniques differ in one of these three categories: network
structure, parameter inference, and decoding/generation. There are also other
concerns, such as efficiency and parallelism for training a model. In this
paper, we provide a comprehensive literature survey on different seq2seq models
for abstractive text summarization from the viewpoint of network structures,
training strategies, and summary generation algorithms. Several models were
first proposed for language modeling and generation tasks, such as machine
translation, and later applied to abstractive text summarization. Hence, we
also provide a brief review of these models. As part of this survey, we also
develop an open source library, namely, Neural Abstractive Text Summarizer
(NATS) toolkit, for the abstractive text summarization. An extensive set of
experiments have been conducted on the widely used CNN/Daily Mail dataset to
examine the effectiveness of several different neural network components.
Finally, we benchmark two models implemented in NATS on the two recently
released datasets, namely, Newsroom and Bytecup.

本研究从网络结构、训练策略和摘要生成算法三个方面全面综述了不同的 seq2seq 模型用于文本摘要生成的研究，并提出了一个名为 NATS 的开源工具包进行研究，对 CNN / Daily Mail 数据集进行了广泛的实验检验，在 Newsroom 和 Bytecup 数据集上测试了两个模型。