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
this https URL

科学新闻报道的自动生成提高了学术洞察的可访问性，本文通过对一组学术出版物与相应科学新闻报道的平行集合进行广泛分析，突出了两者在易读性和简洁性上的差异，并使用先进的文本生成模型对数据集进行了基准测试，为进一步探索科学新闻报道的自动生成奠定了基础。