Sequence-to-sequence models with soft attention have been successfully
applied to a wide variety of problems, but their decoding process incurs a
quadratic time and space cost and is inapplicable to real-time sequence
transduction. To address these issues, we propose Monotonic Chunkwise Attention
(MoChA), which adaptively splits the input sequence into small chunks over
which soft attention is computed. We show that models utilizing MoChA can be
trained efficiently with standard backpropagation while allowing online and
linear-time decoding at test time. When applied to online speech recognition,
we obtain state-of-the-art results and match the performance of a model using
an offline soft attention mechanism. In document summarization experiments
where we do not expect monotonic alignments, we show significantly improved
performance compared to a baseline monotonic attention-based model.

本文介绍了一种名为 MoChA 的注意力机制，该机制可以将输入序列自适应地分成小块，以实现在线和线性时间解码，并在在线语音识别和文档摘要等任务中取得了良好的结果。

单调块级注意力

Monotonic Chunkwise Attention

Current models for document summarization disregard user preferences such as
the desired length, style, the entities that the user might be interested in,
or how much of the document the user has already read. We present a neural
summarization model with a simple but effective mechanism to enable users to
specify these high level attributes in order to control the shape of the final
summaries to better suit their needs. With user input, our system can produce
high quality summaries that follow user preferences. Without user input, we set
the control variables automatically. On the full text CNN-Dailymail dataset, we
outperform state of the art abstractive systems (both in terms of F1-ROUGE1
40.38 vs. 39.53 and human evaluation).

本文提出了一种神经摘要模型，能够通过简单而有效的机制实现用户对文本长度、风格、感兴趣的实体等高级属性的控制，以生成符合用户需求的高质量摘要，并在 CNN-Dailymail 数据集上优于现有的自动化系统和人工评价。

可控抽象摘要

Controllable Abstractive Summarization

We propose a new class of determinantal point processes (DPPs) which can be
manipulated for inference and parameter learning in potentially sublinear time
in the number of items. This class, based on a specific low-rank factorization
of the marginal kernel, is particularly suited to a subclass of continuous DPPs
and DPPs defined on exponentially many items. We apply this new class to
modelling text documents as sampling a DPP of sentences, and propose a
conditional maximum likelihood formulation to model topic proportions, which is
made possible with no approximation for our class of DPPs. We present an
application to document summarization with a DPP on $2^{500}$ items.

该论文提出了一种新的行列式点过程的类别，可用于推理和参数学习，特别适用于在指数级别上定义的文本文档的概率建模。应用该技术进行文档摘要，并对可能达到 2^500 个项目的情况进行了演示。

子线性时间学习行列式点过程

Learning Determinantal Point Processes in Sublinear Time

We present a document compression system that uses a hierarchical
noisy-channel model of text production. Our compression system first
automatically derives the syntactic structure of each sentence and the overall
discourse structure of the text given as input. The system then uses a
statistical hierarchical model of text production in order to drop
non-important syntactic and discourse constituents so as to generate coherent,
grammatical document compressions of arbitrary length. The system outperforms
both a baseline and a sentence-based compression system that operates by
simplifying sequentially all sentences in a text. Our results support the claim
that discourse knowledge plays an important role in document summarization.

我们提出了一种使用层级噪声信道模型的文档压缩系统，该系统可以自动获取输入文本的句法结构和总体话语结构，并使用统计模型生成连贯的、语法正确的文档摘要。我们的实验结果支持话语知识在文档摘要中发挥重要作用的观点。