Abstract neural networks have made significant progress and demonstrated remarkable achievements in the field of text summarization. However, abstract summarization is highly likely to generate summaries of poor fidelity and even deviate from the semantic essence of the source documents due to its flexibility. To address this issue, this study proposes two methods to improve the fidelity of summaries. For Method 1, since entities play an important role in summaries and are usually derived from the original documents, the paper suggests allowing the model to copy entities from the source document to ensure that the generated entities match those in the source document and thereby prevent the generation of inconsistent entities. For Method 2, to better prevent the generated summary from deviating from the original text semantically, the study uses key entities and key tokens as two types of guiding information at different levels of granularity in the summary generation process. The performance of the proposed methods is evaluated using the ROUGE metric on two widely used text summarization datasets, namely, CNNDM and XSum. The experimental results demonstrate that both methods have significantly improved the performance of the model. Furthermore, the experiments also prove that the entity copy mechanism can, to some extent, use guiding information to correct introduced semantic noise.