@inproceedings{yu-etal-2025-hierarchical,
title = "Hierarchical Memory Organization for {W}ikipedia Generation",
author = "Yu, Eugene J. and
Zhu, Dawei and
Song, Yifan and
Wong, Xiangyu and
Zhang, Jiebin and
Shi, Wenxuan and
Li, Xiaoguang and
Liu, Qun and
Li, Sujian",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1423/",
doi = "10.18653/v1/2025.acl-long.1423",
pages = "29404--29427",
ISBN = "979-8-89176-251-0",
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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<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.</abstract>
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%0 Conference Proceedings
%T Hierarchical Memory Organization for Wikipedia Generation
%A Yu, Eugene J.
%A Zhu, Dawei
%A Song, Yifan
%A Wong, Xiangyu
%A Zhang, Jiebin
%A Shi, Wenxuan
%A Li, Xiaoguang
%A Liu, Qun
%A Li, Sujian
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F yu-etal-2025-hierarchical
%X 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.
%R 10.18653/v1/2025.acl-long.1423
%U https://aclanthology.org/2025.acl-long.1423/
%U https://doi.org/10.18653/v1/2025.acl-long.1423
%P 29404-29427
Markdown (Informal)
[Hierarchical Memory Organization for Wikipedia Generation](https://aclanthology.org/2025.acl-long.1423/) (Yu et al., ACL 2025)
ACL
- Eugene J. Yu, Dawei Zhu, Yifan Song, Xiangyu Wong, Jiebin Zhang, Wenxuan Shi, Xiaoguang Li, Qun Liu, and Sujian Li. 2025. Hierarchical Memory Organization for Wikipedia Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29404–29427, Vienna, Austria. Association for Computational Linguistics.