With the advancement of natural language processing technology, text summarization techniques have been widely applied in various aspects of life and play an important role. In the field of judiciary, artificial intelligence has facilitated the development of the judicial system towards informatization and intelligence. Judicial text summarization plays a crucial role in this process by reducing the dimensionality of legal texts, enabling quick understanding of case details, and extracting key elements. However, existing summarization models applied to legal texts often suffer from issues such as poor quality, repetitive and redundant content, and inconsistency with real-world situations. Particularly, when dealing with cases involving multiple charges and penalties against defendants, common summarization models may generate summaries that do not match the actual legal consequences. To address these challenges, a research introduces a legal text summarization model called LCSG-ERNIE (Legal Case Summary Generation Based on Enhanced language Representation with Informative Entities), which incorporates legal knowledge into a pre-trained language model and leverages contrastive learning to generate summaries. Experimental results demonstrate that the proposed model achieves promising performance.