Design of a tool for checking academic integrity and content consistency of paper abstracts
Yang Yu, Ning Li · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021) · 2022
To save costs of manual reviewing, a tool was designed for automatically checking academic integrity and content consistency of abstracts through analyzing the academic problems of abstracts from multi-dimensional testing indicators. First of all, the recognition of abstract knowledge elements can be achieved with the help of the Naive Bayes algorithm and the posterior probability correction method. On this basis, the consistency between the abstract and the text can be checked in combination with semantic matching and knowledge element matching. As can be seen from the experimental results, the F value of the model can reach 0.85 in the academic integrity checking of abstracts. At the same time, the checking granularity is refined to effectively distinguish the abstracts of varying quality.