计算机技术与发展2026，Vol.36Issue(4)：121-129,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0299
基于实体信息增强思维链的政策长文本摘要方法
A Long Policy Text Summarization Method Based on Entity Information Enhancement Chain of Thought
摘要
Abstract
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.关键词
政策公文/文本摘要/大语言模型/思维链/实体抽取/关系建模Key words
policy official documents/text summarization/large language model/chain of thought/entity extraction/relational modeling分类
信息技术与安全科学引用本文复制引用
赵景欣,王志强,武永亮,董佳,唐松..基于实体信息增强思维链的政策长文本摘要方法[J].计算机技术与发展,2026,36(4):121-129,9.基金项目
河北省科学院科技计划项目(25606) （25606）
河北省自然科学基金资助项目(F2024210005) （F2024210005）
