An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model

Zhongyi Wang, Haoxuan Zhang, Jiangping Chen, Haihua Chen

Abstract: Highlights•We propose an effective framework, MNSA-ITMCM, for measuring the novelty of scientific articles.•We define novelty as the organic reorganization of topics, and use cloud model to address the conversion between them.•We validate the effectiveness of MNSA-ITMCM on real-world datasets from two different scientific domains and venues.•MNSA-ITMCM can effectively identify different types of novel papers and accurately predict their novelty levels.