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

Identifying Academic Creative Concept Topics Based on Future Work of Scientific Papers

【目的】以科技论文中未来工作句集为入手点,研究学术创新构想话题自动生成方法,加速创新问题求解。【方法】首先采用规则匹配与BERT相结合的方法从论文全文中抽取未来工作句集;然后通过相关领域论文集进行关联知识扩展计算,发现与未来研究方向相关的关键词、论文等,共同形成创新提要;再利用基于UniLM的文本生成模型自动生成创新构想话题。【结果】经实证验证和专家评估,生成结果的整体创新性平均分为6.04分,整体兴趣度平均分为6.01分,呈正向评价。【局限】基于UniLM的创新构想话题生成模型未嵌入先验语义知识以及没有使用大规模数据实验,生成创新构想话题通顺性及创新性还存在不足。【结论】所提方法探索了学术创新构想话题自动生成的目标,为拓宽科技创新路径提供了新思路。

[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.