  Scientific news reports serve as a bridge, adeptly translating complex
research articles into reports that resonate with the broader public. The
automated generation of such narratives enhances the accessibility of scholarly
insights. In this paper, we present a new corpus to facilitate this paradigm
development. Our corpus comprises a parallel compilation of academic
publications and their corresponding scientific news reports across nine
disciplines. To demonstrate the utility and reliability of our dataset, we
conduct an extensive analysis, highlighting the divergences in readability and
brevity between scientific news narratives and academic manuscripts. We
benchmark our dataset employing state-of-the-art text generation models. The
evaluation process involves both automatic and human evaluation, which lays the
groundwork for future explorations into the automated generation of scientific
news reports. The dataset and code related to this work are available at
https://dongqi.me/projects/SciNews.
