用个人叙事提升冷门人物维基百科质量
REVERSUM: A Multi-staged Retrieval-Augmented Generation Method to Enhance Wikipedia Tail Biographies through Personal Narratives
- 分阶段检索生成，融合个人自述文本增强维基内容
- 人工评估显示信息量提升28.5%，内容融合度高17%
- 适合想改进长尾人物资料的维基编辑者或研究者
维基百科是涵盖广泛实体的宝贵事实资源，但知名度较低的条目（B、C类传记）质量往往不足。本研究提出REVerSum方法，通过多阶段检索增强生成技术，利用自传、他人传记等个人叙事来丰富这些冷门条目的内容。结果表明，个人叙事能显著提升维基条目质量，提供可靠且被长期忽视的信息源。基于众包评估，REVerSum生成内容在与原文整合度上优于最佳基线17%，信息量高出28.5%。代码与数据已公开于https://github.com/sayantan11995/wikipedia_enrichment。
原文摘要 · Abstract (English)
Wikipedia is an invaluable resource for factual information about a wide range of entities. However, the quality of articles on less-known entities often lags behind that of the well-known ones. This study proposes a novel approach to enhancing Wikipedia's B and C category biography articles by leveraging personal narratives such as autobiographies and biographies. By utilizing a multi-staged retrieval-augmented generation technique -- REVerSum -- we aim to enrich the informational content of these lesser-known articles. Our study reveals that personal narratives can significantly improve the quality of Wikipedia articles, providing a rich source of reliable information that has been underutilized in previous studies. Based on crowd-based evaluation, REVerSum generated content outperforms the best performing baseline by 17% in terms of integrability to the original Wikipedia article and 28.5\% in terms of informativeness. Code and Data are available at: https://github.com/sayantan11995/wikipedia_enrichment
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