Automatically Generating Wikipedia Articles: a Structure-Aware Approach
Authors | Christina Joan Sauper Regina Barzilay |
|---|---|
Publication date | 2009 |
DOI | 10.3115/1687878.1687909 |
Links | Original |
Automatically Generating Wikipedia Articles: a Structure-Aware Approach - scientific work related to Wikipedia quality published in 2009, written by Christina Joan Sauper and Regina Barzilay.
Overview
In this paper, authors investigate an approach for creating a comprehensive textual overview of a subject composed of information drawn from the Internet. Authors use the high-level structure of human-authored texts to automatically induce a domain-specific template for the topic structure of a new overview. The algorithmic innovation of work is a method to learn topic-specific extractors for content selection jointly for the entire template. Authors augment the standard perceptron algorithm with a global integer linear programming formulation to optimize both local fit of information into each topic and global coherence across the entire overview. The results of evaluation confirm the benefits of incorporating structural information into the content selection process.
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Wikipedia Quality
Sauper, Christina Joan; Barzilay, Regina. (2009). "[[Automatically Generating Wikipedia Articles: a Structure-Aware Approach]]". Association for Computational Linguistics. DOI: 10.3115/1687878.1687909.
English Wikipedia
{{cite journal |last1=Sauper |first1=Christina Joan |last2=Barzilay |first2=Regina |title=Automatically Generating Wikipedia Articles: a Structure-Aware Approach |date=2009 |doi=10.3115/1687878.1687909 |url=https://wikipediaquality.com/wiki/Automatically_Generating_Wikipedia_Articles:_a_Structure-Aware_Approach |journal=Association for Computational Linguistics}}
HTML
Sauper, Christina Joan; Barzilay, Regina. (2009). "<a href="https://wikipediaquality.com/wiki/Automatically_Generating_Wikipedia_Articles:_a_Structure-Aware_Approach">Automatically Generating Wikipedia Articles: a Structure-Aware Approach</a>". Association for Computational Linguistics. DOI: 10.3115/1687878.1687909.
