Tiziano Piccardi, Michele Catasta, Leila Zia, Robert West
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 and recall at 10 of about 80% in the crowdsourcing evaluation.
Category pruning tool: The tool used to prune the category network according to the concept of purity.
Recommendations Dataset: Recommendations generated by the method category based using category–section counts (sec. 4.1). The repository constains the recommendations by category and by article (simple merge with sum - no L2R)