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We study the task of long-form opinion text generation, which faces at leasttwo distinct challenges. First, existing neural generation models fall short ofcoherence, thus requiring efficient content planning. Second, diverse types ofinformation are needed to guide the generator to cover both subjective andobjective content. To this end, we propose DYPLOC, a generation framework thatconducts dynamic planning of content while generating the output based on anovel design of mixed language models. To enrich the generation with diversecontent, we further propose to use large pre-trained models to predict relevantconcepts and to generate claims. We experiment with two challenging tasks onnewly collected datasets: (1) argument generation with Reddit ChangeMyView, and(2) writing articles using New York Times' Opinion section. Automaticevaluation shows that our model significantly outperforms competitivecomparisons. Human judges further confirm that our generations are morecoherent with richer content.

DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation