ABSTRACT
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 approach 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.
- S. Banerjee, C. Caragea, and P. Mitra. Playscript classification and automatic wikipedia play articles generation. In Proceedings of the 22nd International Conference on Pattern Recognition (ICPR), pages 3630--3635. IEEE, 2014. Google ScholarDigital Library
- S. Banerjee and P. Mitra. Wikikreator: Improving wikipedia stubs automatically. In Proceedings of the Joint Conference of the 53rd Annual Meeting of the ACL and the 7th International Joint Conference on Natural Language Processing of the AFNLP. Association for Computational Linguistics, 2015.Google Scholar
- D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993--1022, 2003. Google ScholarDigital Library
- Y.-l. Boureau, Y. L. Cun, et al. Sparse feature learning for deep belief networks. In Advances in neural information processing systems, pages 1185--1192, 2008.Google ScholarDigital Library
- J. Clarke and M. Lapata. Global inference for sentence compression: An integer linear programming approach. J. Artif. Intell. Res.(JAIR), 31:399--429, 2008. Google ScholarDigital Library
- G. Erkan and D. R. Radev. Lexrank: Graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res.(JAIR), 22(1):457--479, 2004. Google ScholarDigital Library
- C. Kohlschütter, P. Fankhauser, and W. Nejdl. Boilerplate detection using shallow text features. In Proceedings of the third ACM international conference on Web search and data mining, pages 441--450. ACM, 2010. Google ScholarDigital Library
- Q. Le and T. Mikolov. Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning (ICML-14), pages 1188--1196, 2014.Google ScholarDigital Library
- P. Li, Y. Wang, and J. Jiang. Automatically building templates for entity summary construction. Information Processing & Management, 49(1):330--340, 2013. Google ScholarDigital Library
- C.-Y. Lin. Rouge: A package for automatic evaluation of summaries. In Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, pages 74--81, 2004.Google Scholar
- A. Nenkova, S. Maskey, and Y. Liu. Automatic summarization. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts of ACL 2011, page 3. Association for Computational Linguistics, 2011. Google ScholarDigital Library
- C. Sauper and R. Barzilay. Automatically generating wikipedia articles: A structure-aware approach. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1-Volume 1, pages 208--216. Association for Computational Linguistics, 2009. Google ScholarDigital Library
- I. H. Witten, G. W. Paynter, E. Frank, C. Gutwin, and C. G. Nevill-Manning. Kea: Practical automatic keyphrase extraction. In Proceedings of the fourth ACM conference on Digital libraries, pages 254--255. ACM, 1999. Google ScholarDigital Library
- C. Yao, X. Jia, S. Shou, S. Feng, F. Zhou, and H. Liu. Autopedia: automatic domain-independent wikipedia article generation. In Proceedings of the 20th international conference companion on World wide web, pages 161--162. ACM, 2011. Google ScholarDigital Library
Index Terms
- Filling the Gaps: Improving Wikipedia Stubs
Recommendations
Sentiment diversification for short review summarization
WI '17: Proceedings of the International Conference on Web IntelligenceWith the abundance of reviews published on the Web about a given product, consumers are looking for ways to view major opinions that can be presented in a quick and succinct way. Reviews contain many different opinions, making the ability to show a ...
Graph-based abstractive biomedical text summarization
Graphical abstractDisplay Omitted
Highlights- A graph generation and frequent itemset mining approach have been used for the generation of extractive summaries.
AbstractSummarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. ...
SumCR: A new subtopic-based extractive approach for text summarization
In text summarization, relevance and coverage are two main criteria that decide the quality of a summary. In this paper, we propose a new multi-document summarization approach SumCR via sentence extraction. A novel feature called Exemplar is introduced ...
Comments