PST-Bench：追踪并基准化论文来源
PST-Bench: Tracing and Benchmarking the Source of Publications
AI总结：
本文提出论文来源追踪问题，构建高质量计算机科学数据集 PST-Bench，通过实验揭示不同主题的演化模式并指出未来研究方向。
摘要：
追踪研究论文的来源对研究人员而言是一项基础但具有挑战性的任务。论文之间数十亿规模的引用关系阻碍了研究人员高效理解科学演化。迄今为止，仍缺乏一个由专业研究人员构建的准确且可扩展的数据集，用于识别其研究论文的直接来源，并在此基础上开发自动算法以扩展科学的演化知识。本文研究了论文来源追踪（PST）问题，并构建了一个高质量且持续增长的计算机科学数据集 PST-Bench。基于 PST-Bench，我们揭示了若干有趣的发现，例如不同主题之间不同的演化模式。对各种方法的探索凸显了 PST-Bench 的难度，并指出了该主题的潜在方向。数据集和代码已在 https://github.com/THUDM/paper-source-trace 上公开。
英文摘要：
Tracing the source of research papers is a fundamental yet challenging task for researchers. The billion-scale citation relations between papers hinder researchers from understanding the evolution of science efficiently. To date, there is still a lack of an accurate and scalable dataset constructed by professional researchers to identify the direct source of their studied papers, based on which automatic algorithms can be developed to expand the evolutionary knowledge of science. In this paper, we study the problem of paper source tracing (PST) and construct a high-quality and ever-increasing dataset PST-Bench in computer science. Based on PST-Bench, we reveal several intriguing discoveries, such as the differing evolution patterns across various topics. An exploration of various methods underscores the hardness of PST-Bench, pinpointing potential directions on this topic. The dataset and codes have been available at https://github.com/THUDM/paper-source-trace.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
