基于大语言模型的学术论文创新亮点特征解析与自动生成研究OA

[Purpose/significance]In response to the problems of non-standard expression in academic abstracts and lack of standardized methods for writing innovative highlights,this study explores an automatic generation method for innovative highlights based on the large language model.The aim is to provide researchers with diverse expression references and optimization suggestions,assist them in more accurately extracting and presenting research innovation points,improve the accuracy of academic innovation expression,and serve inno-vation content evaluation.[Method/process]Firstly,using the theory of ternary function in systemic functional linguistics,this paper ana-lyzes the innovation highlights from multiple dimensions,such as innovation triggers,core terms,tense voice,sentence structure and abstract complementarity.Secondly,summarize the language expression characteristics of innovative highlights,and form a standardized language expression structure.Finally,the reasoning output of the large language model is optimized by designing the thinking chain according to the language characteristics to realize the automatic generation of innovative highlights.[Result/conclusion]Experimental results demonstrate that the chain-of-thought prompts designed in this study can effectively constrain the quality of text generated by large language models,with the generated research highlights performing well in terms of content richness,logical clarity,and expression accuracy.This research provides an automated solution to assist scholars in efficiently extracting key innovations and supports large-scale innovative highlight generation for publishing institutions.