[Objective]To fully explore the semantic information content of review papers,this study proposes a system of relevant information elements and a formal definition of their extraction tasks.We constructed a corresponding framework to explore the semantic information of review papers.[Methods]To address the issues of high specialization,sparse term distribution,and difficulty in annotation in review papers,we applied multi-task learning to achieve information complementarity across tasks.We also introduced self-supervised learning to discover latent information from unlabeled data.[Results]The proposed multi-task learning framework significantly enhanced the performance of various tasks,especially improving the accuracy of element relationship recognition tasks by 8.32％.Furthermore,the overall F1 score increased by about 2％through self-supervised learning.[Limitations]The information extraction process does not consider non-textual data such as images and tables.[Conclusions]The proposed method and process incorporate multi-task and self-supervised learning to improve the mining effect of labeled data and unlabeled data.
Information ExtractionReading ComprehensionMulti-Task LearningSelf-Supervised Learning
NETL
NSTL
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