NwQM: A neural quality assessment framework for Wikipedia

TL;DR

NwQM is a deep learning framework that assesses Wikipedia article quality by integrating text, metadata, and images, achieving significant improvements over existing methods.

Contribution

The paper introduces NwQM, a novel neural model that combines multiple information sources for more accurate Wikipedia article quality assessment.

Findings

8% improvement over state-of-the-art methods

Effective integration of text, metadata, and images

Detailed ablation studies validating the approach

Abstract

Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic content. This manual designation is an onerous task because it necessitates profound knowledge about encyclopedic language, as well navigating circuitous set of wiki guidelines. In this paper we propose Neural wikipedia QualityMonitor (NwQM), a novel deep learning model which accumulates signals from several key information sources such as article text, meta data and images to obtain improved Wikipedia article representation. We present comparison of our approach against a plethora of available solutions and show 8% improvement over state-of-the-art approaches with detailed ablation studies.