  With the development of legal intelligence, Criminal Court View Generation
has attracted much attention as a crucial task of legal intelligence, which
aims to generate concise and coherent texts that summarize case facts and
provide explanations for verdicts. Existing researches explore the key
information in case facts to yield the court views. Most of them employ a
coarse-grained approach that partitions the facts into broad segments (e.g.,
verdict-related sentences) to make predictions. However, this approach fails to
capture the complex details present in the case facts, such as various criminal
elements and legal events. To this end, in this paper, we propose an Event
Grounded Generation (EGG) method for criminal court view generation with
cooperative (Large) Language Models, which introduces the fine-grained event
information into the generation. Specifically, we first design a LLMs-based
extraction method that can extract events in case facts without massive
annotated events. Then, we incorporate the extracted events into court view
generation by merging case facts and events. Besides, considering the
computational burden posed by the use of LLMs in the extraction phase of EGG,
we propose a LLMs-free EGG method that can eliminate the requirement for event
extraction using LLMs in the inference phase. Extensive experimental results on
a real-world dataset clearly validate the effectiveness of our proposed method.
