基于深度学习模型的标志性论文发现研究

27th International Conference on Science, Technology and Innovation Indicators (STI 2023) Pub Date : 2023-05-14 DOI:10.55835/64382c384cc7592973d4786e

Yucheng Chen, Zhengyi Guan, Chundong Li, Chenyang Wang, Zhe Wang

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引用次数: 0

摘要

本文提出了一种选择标志性论文的新方法。我们使用章节结构识别技术，并将其与流行的深度学习模型相结合来识别论文的章节结构。基于这个识别结果，我们计算出每篇论文在所有文章中被提及次数的总和，发现被提及频率高的标志性论文。我们将重点放在“Method”和“Conclusion”这两个部分，寻找在这两个部分中被频繁提及的论文，确定标志性论文。通过这种方法，我们可以更准确地发现和评价重要的研究成果，为相关领域的学者提供更有价值的参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Study on Iconic Paper Discovery Based on Deep Learning Models

This paper proposes a new method for selecting iconic papers. We use chapter structure recognition technology and combine it with popular deep learning models to identify the chapter structure of the papers. Based on this identification result, we calculate the sum of the number of times each paper is mentioned in all articles, to discover iconic papers with high mention frequency. We focus on the "Method" and "Conclusion" sections to find papers that are frequently mentioned in these two sections and determine iconic papers. With this method, we can more accurately discover and evaluate important research results and provide more valuable references for scholars in related fields.

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来源期刊

27th International Conference on Science, Technology and Innovation Indicators (STI 2023)

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