AutoJourn让AI新闻生成更客观，自动抓取多视角并消除偏见。
AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism
- 用提示工程+检索增强提取社交媒体中的多元观点
- 生成兼顾冲突立场的平衡摘要，显著降低偏见
- 支持实时检测与自动中和新闻中的立场偏见，适合媒体机构使用
我们提出AutoJourn，一个用于多视角新闻生成与偏见感知评估的演示系统，面向负责任的自动化新闻。该系统解决三大挑战：从非结构化社交媒体讨论中提取多样视角，生成保留观点多样性的摘要，以及检测或缓解生成新闻中的偏见。流程整合了先进提示工程与可选检索增强，以生成语义多样化的视角集合；采用多视角摘要模块，将对立观点融合为平衡摘要；配备偏见分析套件，支持句子级偏见检测、类型分类及自动中和。用户可在界面中查看视角聚类、对比立场相关摘要、生成新闻文章，并应用偏见感知重写。我们通过内在指标（语义多样性、摘要质量、偏见减少）评估各组件，结果优于强基线且保持内容忠实性。论文附带公开可访问的实时演示，促进可复现性与社会负责任自动化新闻研究。
原文摘要 · Abstract (English)
We present AutoJourn, a demonstration system for multi-perspective news generation and bias-aware evaluation using large language models (LLMs). The system tackles three core challenges in responsible automated journalism: extracting diverse perspectives from unstructured social media discussions, generating summaries that preserve viewpoint diversity, and detecting or mitigating bias in AI-generated news. The pipeline integrates advanced prompt engineering with optional retrieval augmentation to produce semantically diverse perspective sets, a multi-perspective summarisation module that merges conflicting viewpoints into balanced summaries, and a bias analysis suite supporting sentence-level bias detection and type classification in the generated news article, and automatic neutralisation. Users can inspect perspective clusters, compare stance-specific summaries, generate news articles, and apply bias-aware rewrites directly in the interface. We evaluate each component with intrinsic metrics -- semantic diversity, summary quality, and bias reduction and show improvements over strong baselines while maintaining content fidelity. A live, publicly accessible demo accompanies the paper to facilitate reproducibility and further research on socially responsible automated journalism.
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