Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text
  Generation   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.

24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an