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This paper summarizes our studies on propaganda detection techniques for newsarticles in the SemEval-2020 task 11. This task is divided into the SI and TCsubtasks. We implemented the GloVe word representation, the BERT pretrainingmodel, and the LSTM model architecture to accomplish this task. Our approachachieved good results for both the SI and TC subtasks. The macro-F1-score forthe SI subtask is 0.406, and the micro-F1-score for the TC subtask is 0.505.Our method significantly outperforms the officially released baseline method,and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set.This paper also compares the performances of different deep learning modelarchitectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on thedetection of news promotion techniques. The code of this paper is availabledat: https://github.com/daojiaxu/semeval_11.

YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles