Filling the gaps: Improving wikipedia stubs

Siddhartha Banerjee, Prasenjit Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


The availability of only a limited number of contributors on Wikipedia cannot ensure consistent growth and improvement of the online encyclopedia. With information being scattered on the web, our goal is to automate the process of generation of content for Wikipedia. In this work, we propose a technique of improving stubs on Wikipedia that do not contain comprehensive information. A classifier learns features from the existing comprehensive articles on Wikipedia and recommends content that can be added to the stubs to improve the completeness of such stubs. We conduct experiments using several classifiers-Latent Dirichlet Allocation (LDA) based model, a deep learning based architecture (Deep belief network) and TFIDF based classifier. Our experiments reveal that the LDA based model outperforms the other models (6% F-score). Our generation ap-proach shows that this technique is capable of generating comprehensive articles. ROUGE-2 scores of the articles generated by our system outperform the articles generated using the baseline. Content generated by our system has been appended to several stubs and successfully retained in Wikipedia.

Original languageEnglish (US)
Title of host publicationDocEng 2015 - Proceedings of the 2015 ACM Symposium on Document Engineering
PublisherAssociation for Computing Machinery, Inc
Number of pages4
ISBN (Electronic)9781450333078
StatePublished - Sep 8 2015
EventACM Symposium on Document Engineering, DocEng 2015 - Lausanne, Switzerland
Duration: Sep 8 2015Sep 11 2015

Publication series

NameDocEng 2015 - Proceedings of the 2015 ACM Symposium on Document Engineering


OtherACM Symposium on Document Engineering, DocEng 2015

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Software


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