用一致性评分和结构化摘要提升新闻生成的准确与专业性
ScoreRAG: A Retrieval-Augmented Generation Framework with Consistency-Relevance Scoring and Structured Summarization for News Generation
- 通过检索增强生成+大模型评分，筛选高相关性新闻文档
- 基于评分重排并过滤低质内容，提升生成文章可靠性
- 适合需要高可信度自动新闻生产的媒体与机构
本研究提出ScoreRAG，一种提升自动化新闻生成质量的方法。尽管自然语言处理和大语言模型取得进展，现有新闻生成方法仍常出现幻觉、事实不一致及领域专长不足问题。ScoreRAG通过多阶段框架解决上述挑战：首先从向量数据库中检索相关新闻文档，将其映射为完整新闻条目，并基于大语言模型评估分配一致性相关性分数；随后按相关性重新排序，剔除低质量条目；最后根据得分生成分级摘要，引导大模型产出符合专业新闻标准的完整文章。该方法显著提升生成新闻的准确性、连贯性、信息量与专业性，同时保证生成过程的稳定性和一致性。代码与演示已开源。
原文摘要 · Abstract (English)
This research introduces ScoreRAG, an approach to enhance the quality of automated news generation. Despite advancements in Natural Language Processing and large language models, current news generation methods often struggle with hallucinations, factual inconsistencies, and lack of domain-specific expertise when producing news articles. ScoreRAG addresses these challenges through a multi-stage framework combining retrieval-augmented generation, consistency relevance evaluation, and structured summarization. The system first retrieves relevant news documents from a vector database, maps them to complete news items, and assigns consistency relevance scores based on large language model evaluations. These documents are then reranked according to relevance, with low-quality items filtered out. The framework proceeds to generate graded summaries based on relevance scores, which guide the large language model in producing complete news articles following professional journalistic standards. Through this methodical approach, ScoreRAG aims to significantly improve the accuracy, coherence, informativeness, and professionalism of generated news articles while maintaining stability and consistency throughout the generation process. The code and demo are available at: https://github.com/peiyun2260/ScoreRAG.
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