Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation   Generating texts in scientific papers requires not only capturing the content
contained within the given input but also frequently acquiring the external
information called \textit{context}. We push forward the scientific text
generation by proposing a new task, namely \textbf{context-aware text
generation} in the scientific domain, aiming at exploiting the contributions of
context in generated texts. To this end, we present a novel challenging
large-scale \textbf{Sci}entific Paper Dataset for Conte\textbf{X}t-Aware Text
\textbf{Gen}eration (SciXGen), consisting of well-annotated 205,304 papers with
full references to widely-used objects (e.g., tables, figures, algorithms) in a
paper. We comprehensively benchmark, using state-of-the-arts, the efficacy of
our newly constructed SciXGen dataset in generating description and paragraph.
Our dataset and benchmarks will be made publicly available to hopefully
facilitate the scientific text generation research.

35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an