通过文本和图像分析的高影响力机器识别研究
Machine Identification of High Impact Research through Text and Image Analysis
摘要：
学术论文提交和出版的数量正在以越来越快的速度增长。虽然大量的研究有望在各个领域取得进展，但大量的输出本质上增加了噪音。我们提出了一个系统，自动分离的论文与那些获得引用的可能性低，作为一种手段，以快速找到高影响力，高质量的研究。我们的系统同时使用了一个视觉分类器，用于推测文档的整体外观，和一个文本分类器，用于做出内容知情的决定。该领域目前的工作重点是由个别会议的论文组成的小型数据集。尝试在较大的数据集上使用类似的技术通常只考虑文档的摘录，例如摘要，可能会丢弃有价值的数据。我们通过提供一个由PDF文档和引文计数组成的数据集来纠正这些问题，该数据集跨越了两个独立的学术领域：计算机科学和医学。这个新的数据集使我们能够通过跨时间和学术领域的概括来扩展该领域的现有工作。此外，我们探索了域间预测模型-评估分类器在未训练的域上的性能-以进一步了解这个重要问题。
英文摘要：
The volume of academic paper submissions and publications is growing at an ever increasing rate. While this flood of research promises progress in various fields, the sheer volume of output inherently increases the amount of noise. We present a system to automatically separate papers with a high from those with a low likelihood of gaining citations as a means to quickly find high impact, high quality research. Our system uses both a visual classifier, useful for surmising a document's overall appearance, and a text classifier, for making content-informed decisions. Current work in the field focuses on small datasets composed of papers from individual conferences. Attempts to use similar techniques on larger datasets generally only considers excerpts of the documents such as the abstract, potentially throwing away valuable data. We rectify these issues by providing a dataset composed of PDF documents and citation counts spanning a decade of output within two separate academic domains: computer science and medicine. This new dataset allows us to expand on current work in the field by generalizing across time and academic domain. Moreover, we explore inter-domain prediction models - evaluating a classifier's performance on a domain it was not trained on - to shed further insight on this important problem.
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。
