Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Facts2Story: Controlling Text Generation by Key Facts   Recent advancements in self-attention neural network architectures have
raised the bar for open-ended text generation. Yet, while current methods are
capable of producing a coherent text which is several hundred words long,
attaining control over the content that is being generated -- as well as
evaluating it -- are still open questions. We propose a controlled generation
task which is based on expanding a sequence of facts, expressed in natural
language, into a longer narrative. We introduce human-based evaluation metrics
for this task, as well as a method for deriving a large training dataset. We
evaluate three methods on this task, based on fine-tuning pre-trained models.
We show that while auto-regressive, unidirectional Language Models such as GPT2
produce better fluency, they struggle to adhere to the requested facts. We
propose a plan-and-cloze model (using fine-tuned XLNet) which produces
competitive fluency while adhering to the requested content.

8
The categories of this paper are: hep-ex
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg