AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism

Researchers present AutoJourn, a system for multi-perspective news generation and bias-aware evaluation using large language models, that extracts diverse perspectives from social media, generates balanced summaries, and detects or mitigates bias in AI-generated news. The pipeline shows improvements over baselines in semantic diversity, summary quality, and bias reduction while maintaining content fidelity, with a live demo available.

arXiv:2607.18983v1 Announce Type: new Abstract: 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.