In this paper, we focus on a new practical task, document-scale text content
manipulation, which is the opposite of text style transfer and aims to preserve
text styles while altering the content. In detail, the input is a set of
structured records and a reference text for describing another recordset. The
output is a summary that accurately describes the partial content in the source
recordset with the same writing style of the reference. The task is
unsupervised due to lack of parallel data, and is challenging to select
suitable records and style words from bi-aspect inputs respectively and
generate a high-fidelity long document. To tackle those problems, we first
build a dataset based on a basketball game report corpus as our testbed, and
present an unsupervised neural model with interactive attention mechanism,
which is used for learning the semantic relationship between records and
reference texts to achieve better content transfer and better style
preservation. In addition, we also explore the effectiveness of the
back-translation in our task for constructing some pseudo-training pairs.
Empirical results show superiority of our approaches over competitive methods,
and the models also yield a new state-of-the-art result on a sentence-level
dataset.

本文提出了一个新的实际任务，文档级文本内容操作，它旨在在更改内容的同时保留文本样式，并提出一种基于交互式注意机制的无监督神经模型，探索了在该任务中构建伪训练对的后向翻译方法的有效性。实证结果表明，该方法优于竞争方法，并且模型还在句子级数据集上产生了新的最优结果