The extractive summary model is relatively redundant and the abstractive summary model often loses key information and has inaccurate summary and repeated generated content in long text automatic summarization task. In order to solve these problems, a Reinforced Automatic Summarization model based on Advantage Actor-Critic algorithm (A2C-RLAS) for long text was proposed. Firstly, the key sentences of the original text were extracted by the extractor based on the hybrid neural network of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). Then, the key sentences were refined by the rewriter based on the copy mechanism and the attention mechanism. Finally, the Advantage Actor-Critic (A2C) algorithm in reinforcement learning was used to train the entire network, and the semantic similarity between the rewritten summary and the reference summary (BERTScore (Evaluating Text Generation with Bidirectional Encoder Representations from Transformers) value) was used as a reward to guide the extraction process, so as to improve the quality of sentences extracted by the extractor. The experimental results on CNN/Daily Mail dataset show that, compared with models such as Reinforcement Learning-based Extractive Summarization (Refresh) model, a Recurrent Neural Network based sequence model for extractive summarization (SummaRuNNer) and Distributional Semantics Reward (DSR) model, the A2C-RLAS has the final summary with content more accurate, language more fluent and redundant content effectively reduced, at the same time, A2C-RLAS has both the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BERTScore indicators improved. Compared to the Refresh model and the SummaRuNNer model, the ROUGE-L value of the A2C-RLAS model is increased by 6.3% and 10.2% respectively; compared with the DSR model, the F1 value of the A2C-RLAS model is increased by 30.5%.