Building effective text generation systems requires three critical
components: content selection, text planning, and surface realization, and
traditionally they are tackled as separate problems. Recent all-in-one style
neural generation models have made impressive progress, yet they often produce
outputs that are incoherent and unfaithful to the input. To address these
issues, we present an end-to-end trained two-step generation model, where a
sentence-level content planner first decides on the keyphrases to cover as well
as a desired language style, followed by a surface realization decoder that
generates relevant and coherent text. For experiments, we consider three tasks
from domains with diverse topics and varying language styles: persuasive
argument construction from Reddit, paragraph generation for normal and simple
versions of Wikipedia, and abstract generation for scientific articles.
Automatic evaluation shows that our system can significantly outperform
competitive comparisons. Human judges further rate our system generated text as
more fluent and correct, compared to the generations by its variants that do
not consider language style.

通过端到端训练的两步生成模型，即首先由句子级内容规划器决定涵盖的关键词组和所需的语言风格，然后由表面实现解码器生成相关和连贯的文本，可以显著优于现有竞争模型，在 Reddit 的说服性论证、维基百科的正常和简单版本的段落生成以及科学文章的摘要生成等任务中得到验证。