Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sentence Ordering and Coherence Modeling using Recurrent Neural Networks   Modeling the structure of coherent texts is a key NLP problem. The task of
coherently organizing a given set of sentences has been commonly used to build
and evaluate models that understand such structure. We propose an end-to-end
unsupervised deep learning approach based on the set-to-sequence framework to
address this problem. Our model strongly outperforms prior methods in the order
discrimination task and a novel task of ordering abstracts from scientific
articles. Furthermore, our work shows that useful text representations can be
obtained by learning to order sentences. Visualizing the learned sentence
representations shows that the model captures high-level logical structure in
paragraphs. Our representations perform comparably to state-of-the-art
pre-training methods on sentence similarity and paraphrase detection tasks.

13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex