Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: TED: A Pretrained Unsupervised Summarization Model with Theme Modeling
  and Denoising   Text summarization aims to extract essential information from a piece of text
and transform the text into a concise version. Existing unsupervised
abstractive summarization models leverage recurrent neural networks framework
while the recently proposed transformer exhibits much more capability.
Moreover, most of previous summarization models ignore abundant unlabeled
corpora resources available for pretraining. In order to address these issues,
we propose TED, a transformer-based unsupervised abstractive summarization
system with pretraining on large-scale data. We first leverage the lead bias in
news articles to pretrain the model on millions of unlabeled corpora. Next, we
finetune TED on target domains through theme modeling and a denoising
autoencoder to enhance the quality of generated summaries. Notably, TED
outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English
Gigaword datasets with various document styles. Further analysis shows that the
summaries generated by TED are highly abstractive, and each component in the
objective function of TED is highly effective.

16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th