Abstract: To address the low efficiency of manual summarization caused by lengthy texts, complex topics, and excessive redundant information in ideological and political case texts amid the information explosion, and the limitations of existing methods-purely extractive models overlook the logical connections between sentences, while purely generative models tend to deviate from policy guidance, a CLUSTER2SENT(CLUSTER to SENTence) algorithm is proposed. This algorithm realizes the automatic generation of summaries for ideological and political cases through a three step process: 1) extracting important utterances relevant to each part of the summary, 2) performing clustering analysis on the extracted important utterances, 3) generating summary text for each cluster. Experimental results show that the CLUSTER2SENT algorithm outperforms its purely extractive counterpart by 8 percentage points in the ROUGE-1 metric, which verifies the effectiveness of the algorithm. The study indicates that constructing a section structure when building a summary corpus can significantly improve model performance.

Key words: ideological and political education cases, extractive method, generative method, section structure, CLUSTER2SENT algorithm