Structuring Wikipedia Articles with Section Recommendations

TL;DR

This paper introduces a system for recommending Wikipedia article sections by leveraging similarity measures like topic modeling, collaborative filtering, and category systems, significantly aiding editors in article structuring.

Contribution

It presents novel approaches for section recommendation in Wikipedia, especially highlighting the effectiveness of category-based similarity with high precision.

Findings

Category-based similarity achieves about 80% precision@10.

Multiple similarity measures were explored, with category-based performing best.

The system effectively supports editors in structuring articles.

Abstract

Sections are the building blocks of Wikipedia articles. They enhance readability and can be used as a structured entry point for creating and expanding articles. Structuring a new or already existing Wikipedia article with sections is a hard task for humans, especially for newcomers or less experienced editors, as it requires significant knowledge about how a well-written article looks for each possible topic. Inspired by this need, the present paper defines the problem of section recommendation for Wikipedia articles and proposes several approaches for tackling it. Our systems can help editors by recommending what sections to add to already existing or newly created Wikipedia articles. Our basic paradigm is to generate recommendations by sourcing sections from articles that are similar to the input article. We explore several ways of defining similarity for this purpose (based on topic modeling, collaborative filtering, and Wikipedia's category system). We use both automatic and human evaluation approaches for assessing the performance of our recommendation system, concluding that the category-based approach works best, achieving precision@10 of about 80% in the human evaluation.