Wikipedia is the world's largest online encyclopedia, but maintaining article
quality through collaboration is challenging. Wikipedia designed a quality
scale, but with such a manual assessment process, many articles remain
unassessed. We review existing methods for automatically measuring the quality
of Wikipedia articles, identifying and comparing machine learning algorithms,
article features, quality metrics, and used datasets, examining 149 distinct
studies, and exploring commonalities and gaps in them. The literature is
extensive, and the approaches follow past technological trends. However,
machine learning is still not widely used by Wikipedia, and we hope that our
analysis helps future researchers change that reality.

维基百科是世界上最大的在线百科全书，但通过合作维护文章质量是具有挑战性的。本文回顾了现有的自动测量维基百科文章质量的方法，并识别和比较了机器学习算法、文章特征、质量指标和使用的数据集，检查了 149 个独立的研究，并探讨了它们之间的共同点和差距。然而，机器学习在维基百科中仍未被广泛使用，我们希望我们的分析能帮助未来的研究人员改变这种现状。

维基百科文章的自动质量评估 —— 系统文献综述

Automatic Quality Assessment of Wikipedia Articles -- A Systematic  Literature Review

With so many articles of varying qualities being produced every moment, it is
a very urgent task to screen outstanding articles and commit them to social
media. To our best knowledge, there is a lack of datasets and mature research
works in identifying high-quality articles. Consequently, we conduct some
surveys and finalize 7 objective indicators to annotate the quality of 10k
articles. During annotation, we find that many characteristics of high-quality
articles (e.g., background) rely more on extensive external knowledge than
inner semantic information of articles. In response, we link extracted article
entities to Baidu Encyclopedia, then propose Knowledge Enhanced article Quality
Identification (KE-QI) dataset. To make better use of external knowledge, we
propose a compound model which fuses the text and external knowledge
information via a gate unit to classify the quality of an article. Our
experimental results on KE-QI show that with initialization of our pre-trained
Node2Vec model, our model achieves about 78\% $F_1$, outperforming other
baselines.

该研究为解决鉴别高质量文章的问题提出了一种基于外部知识的文章质量鉴定方法，并提出了相关的数据集和实体链接技术，实验结果表明该方法有效性较高。

KE-QI: 知识增强的文章质量识别数据集

KE-QI: A Knowledge Enhanced Article Quality Identification Dataset

We propose an edit-centric approach to assess Wikipedia article quality as a
complementary alternative to current full document-based techniques. Our model
consists of a main classifier equipped with an auxiliary generative module
which, for a given edit, jointly provides an estimation of its quality and
generates a description in natural language. We performed an empirical study to
assess the feasibility of the proposed model and its cost-effectiveness in
terms of data and quality requirements.

该研究提出了一种基于编辑为中心的方法来评估维基百科文章质量的模型，该模型包含一个主分类器和一个辅助生成模块，可以提供编辑质量的估计和自然语言描述，经过实证研究发现该模型在数据和质量要求方面具有成本效益。