基于实体信息增强思维链的政策长文本摘要方法OA

With the deepening of digital governance,the efficient interpretation of policy official documents has become the key requirement of government affairs and social governance,and text summarization technology has become an important method to quickly extract the core content.However,the length of policy official documents is long and the elements are complex.In view of the inefficiency of traditional manual summarization and the problems of entity omission and logical fault when dealing with long-text policies in general large model,a framework for generating long-text summaries of policy official documents based on entity information enhancement chain of thought is proposed,which consists of three core modules.Firstly,the key elements are extracted through entity ex-traction module to guide the large model to pay attention to the core content.Then,through the relationship modeling module,the semantic association is constructed,and the internal logic of official documents is sorted out.Finally,the final document summary is generated by the prompt generation module,and the key information of the policy text is accurately extracted and the summary is generated.The experimental results show that the F1 values of the framework on Rouge-1,Rouge-2,Rouge-L and BERTScore reach 62.48%,33.02%,34.54%and 75.34%,respectively,which are significantly better than that of other comparison models,and are improved by 4.38 percentage points,7.1 percentage points,10.9 percentage points,and 5.37 percentage points respectively compared with the basic Qwen2.5-7B model.The example of generating summaries shows that the summaries generated by the framework are well characterized in terms of accuracy,completeness of details and language fluency.