Paper Info
Title
Using natural language generation to bootstrap missing Wikipedia articles: A human-centric perspective
Abstract
Nowadays natural language generation (NLG) is used in everything from news reporting and chatbots to social media management. Recent advances in machine learning have made it possible to train NLG systems that seek to achieve human-level performance in text writing and summarisation. In this paper, we propose such a system in the context of Wikipedia and evaluate it with Wikipedia readers and editors. Our solution builds upon the ArticlePlaceholder, a tool used in 14 under-resourced Wikipedia language versions, which displays structured data from the Wikidata knowledge base on empty Wikipedia pages. We train a neural network to generate an introductory sentence from the Wikidata triples shown by the ArticlePlaceholder, and explore how Wikipedia users engage with it. The evaluation, which includes an automatic, a judgement-based, and a task-based component, shows that the summary sentences score well in terms of perceived fluency and appropriateness for Wikipedia, and can help editors bootstrap new articles. It also hints at several potential implications of using NLG solutions in Wikipedia at large, including content quality, trust in technology, and algorithmic transparency.
Year
DOI
Venue
2022
10.3233/SW-210431
SEMANTIC WEB
Keywords
DocType
Volume
Wikipedia, Wikidata, ArticlePlaceholder, multilingual, natural language generation, neural networks
Journal
13
Issue
ISSN
Citations 
2
1570-0844
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Lucie-Aimée Kaffee1104.32
Pavlos Vougiouklis2135.00
Elena Simperl31069122.60