Hierarchical Memory Organization for Wikipedia Generation

• URL: http://arxiv.org/abs/2506.23393v1
• Date: Sun, 29 Jun 2025 20:22:49 GMT
• Title: Hierarchical Memory Organization for Wikipedia Generation
• Authors: Eugene J. Yu, Dawei Zhu, Yifan Song, Xiangyu Wong, Jiebin Zhang, Wenxuan Shi, Xiaoguang Li, Qun Liu, Sujian Li,
• Abstract summary: This paper introduces the Memory Organization-based Generation (MOG) framework to generate Wikipedia articles autonomously.<n>MOG extracts fine-grained memory units from web documents, organizes them into a Wikipedia-style hierarchical structure, and uses this structure to guide the generation process.<n> Evaluations on our newly created WikiStart dataset demonstrate that MOG outperforms baseline methods in producing informative and reliable articles.
• Score: 41.60777339440196
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating Wikipedia articles autonomously is a challenging task requiring the integration of accurate, comprehensive, and well-structured information from diverse sources. This paper introduces the Memory Organization-based Generation (MOG) framework, a novel approach to address these challenges by leveraging a hierarchical memory architecture. MOG extracts fine-grained memory units from web documents, recursively organizes them into a Wikipedia-style hierarchical structure, and uses this structure to guide the generation process. This ensures alignment between memory and the article outline, improving both informativeness and verifiability while minimizing hallucinations. Additionally, a citation module is implemented to enhance traceability by linking every generated sentence to specific memory units. Evaluations on our newly created WikiStart dataset demonstrate that MOG outperforms baseline methods in producing informative and reliable articles, making it particularly robust in real-world scenarios.

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