A Study on Iconic Paper Discovery Based on Deep Learning Models

Autor: YUCHENG CHEN, Zhengyi Guan, Chundong Li, chenyang wang, Zhe Wang
Rok vydání: 2023
Zdroj: 27th International Conference on Science, Technology and Innovation Indicators (STI 2023).
DOI: 10.55835/64382c384cc7592973d4786e
Popis: 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.
Databáze: OpenAIRE