Research on the Recognition of Innovative Contribution Sentences of Academic Papers postprint

Abstract: [Purpose/significance] Contribution sentences of academic papers are elements to reflect the novelty and academic value of papers. This study takes the full text of academic papers and MeSH terms as data sources and uses natural language processing and deep learning techniques to achieve academic paper contribution sentence recognition. This study lays the foundation for fine-grained mining of innovative contents of academic texts, which is important for realizing the evaluation of academic papers based on cognitive computing.[Method/process] Firstly, the full-text PubMed papers were used as the data source for element analysis and feature extraction of the contributed sentences. Secondly, a semi-automatic approach was used to fulfill the data annotation. Finally, the automatic recognition of contributed sentences was realized based on Albert deep learning model.[Result/conclusion] The plausibility of the experimentally labeled training data is proved by the data consistency test, and the experimental results show that the automatic recognition model trained in this paper can identify the contribution sentences in academic papers more effectively compared with other deep learning models.