  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.
