Identifying Academic Creative Concept Topics Based on Future Work of Scientific Papers
Song Ruoxuan,Qian Li(),Du Yu
Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
[Objective] This paper analyzes the sentences on future work from scientific papers, aiming to automatically generate academic innovation ideas. [Methods] First, we combined rule matching with BERT to extract sentences on future work from papers. Then, we conducted the expansion calculation on papers in related fields, and identified keywords and papers on future directions. Finally, these innovative raw materials were fed to the UniLM-based model to create topics of innovation concepts. [Results] The average innovation score of the generated results is 6.04 points, and the average interest level score is 6.01 points. [Limitations] The topic generation model neither includes prior semantic knowledge nor uses large-scale data for experiment, and the quality of generated topics needs to be improved. [Conclusions] The proposed method provides a new idea to expand technological innovation.
宋若璇,钱力,杜宇. 基于科技论文中未来工作句集的学术创新构想话题自动生成方法研究*[J]. 数据分析与知识发现, 2021, 5(5): 10-20.
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