We study the task of long-form opinion text generation, which faces at least
two distinct challenges. First, existing neural generation models fall short of
coherence, thus requiring efficient content planning. Second, diverse types of
information are needed to guide the generator to cover both subjective and
objective content. To this end, we propose DYPLOC, a generation framework that
conducts dynamic planning of content while generating the output based on a
novel design of mixed language models. To enrich the generation with diverse
content, we further propose to use large pre-trained models to predict relevant
concepts and to generate claims. We experiment with two challenging tasks on
newly collected datasets: (1) argument generation with Reddit ChangeMyView, and
(2) writing articles using New York Times' Opinion section. Automatic
evaluation shows that our model significantly outperforms competitive
comparisons. Human judges further confirm that our generations are more
coherent with richer content.

提出了 DYPLOC 框架，采用混合语言模型的新设计，动态规划内容并使用大型预训练模型来生成涵盖多样内容的观点文本。在 Reddit ChangeMyView 的论据生成和 New York Times' Opinion 部分的文章写作任务上，自动评估表明我们的模型显着优于竞争比较，并获得了人类评判者的更准确，内容更丰富的结果。