  Court View Generation (CVG) is a challenging task in the field of Legal
Artificial Intelligence (LegalAI), which aims to generate court views based on
the plaintiff claims and the fact descriptions. While Pretrained Language
Models (PLMs) have showcased their prowess in natural language generation,
their application to the complex, knowledge-intensive domain of CVG often
reveals inherent limitations. In this paper, we present a novel approach, named
Knowledge Injection and Guidance (KIG), designed to bolster CVG using PLMs. To
efficiently incorporate domain knowledge during the training stage, we
introduce a knowledge-injected prompt encoder for prompt tuning, thereby
reducing computational overhead. Moreover, to further enhance the model's
ability to utilize domain knowledge, we employ a generating navigator, which
dynamically guides the text generation process in the inference stage without
altering the model's architecture, making it readily transferable.
Comprehensive experiments on real-world data demonstrate the effectiveness of
our approach compared to several established baselines, especially in the
responsivity of claims, where it outperforms the best baseline by 11.87%.
