Abstract: With the continuous development of science and technology, general artificial intelligence (AGI) technology has demonstrated its powerful capabilities in language understanding and generation. In the judicial field, artificial intelligence also plays an increasingly important role, gradually transition- ing from judicial informatization to judicial intellectualization and smart judicial services. In this transition process, the summarization of judicial texts is a key task. Generating summaries based on judicial texts can achieve the goal of “dimensionality reduction”, help quickly grasp case details and obtain case elements, and provide support for practitioners to efficiently acquire information. However, current judicial text summarization technologies still have some problems, such as: the generated summaries lack legal provisions as the basis for judgment, and the summaries have grammatical errors and incoherent sentences, which lead to poor readability, among other issues. To solve the above problems, this paper  leverages the excellent language understanding and generation capabilities of large language models (LLMs), combines different fine-tuning technologies, and designs different prompt templates to construct a domain-specific large model for judicial text summarization. Verification on various datasets proves the feasibility of this model, providing a potential approach for the integration of large language models and the judicial field.

Key words: text summarization, smart courts, large language model, parameter fine-tuning