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.