Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network. However, scientific papers are full of uncommon domain-specific terms, making it almost impossible for the model to understand its true meaning without the help of the relevant research community. In this paper, we redefine the task of scientific papers summarization by utilizing their citation graph and propose a citation graph-based summarization model CGSum which can incorporate the information of both the source paper and its references. In addition, we construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph. Extensive experiments show that our model can achieve competitive performance when compared with the pretrained models even with a simple architecture. The results also indicates the citation graph is crucial to better understand the content of papers and generate high-quality summaries.
翻译：科学领域以往的文本摘要工作主要侧重于投入文件的内容,但很少考虑其引用网络。然而,科学论文充满了不寻常的域名术语,使得模型几乎不可能在没有相关研究界帮助下理解其真实含义。在本文中,我们重新定义科学论文摘要的任务,利用它们的引用图,并提议一个以引用图为基础的概括模型CGSum,该模型可以纳入源文件及其参考文献的信息。此外,我们建造了一个新的科学论文摘要数据集精度学者网络(SSN),其中包括不同领域的141K研究论文和661K引用关系。整个数据集构成一个大连接引用图。广泛的实验表明,即使使用简单的结构,我们的模型也能够与经过预先训练的模型相比取得竞争性的性能。结果还表明,引用图对于更好地了解文件的内容和产生高质量的摘要至关重要。
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