Automatically Generating Wikipedia Articles: A Structure-Aware Approach

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 208–216, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Automatically Generating Wikipedia Articles: A Structure-Aware Approach Christina Sauper and Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {csauper,regina}@csail.mit.edu Abstract In this paper, we investigate an ap- proach for creating a comprehensive tex- tual overview of a subject composed of in- formation drawn from the Internet. We 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 innova- tion of our work is a method to learn topic- specific extractors for content selection jointly for the entire template. We aug- ment the standard perceptron algorithm with a global integer linear programming formulation to optimize both local fit of information into each topic and global co- herence across the entire overview. The results of our evaluation confirm the bene- fits of incorporating structural information into the content selection process. 1 Introduction In this paper, we consider the task of automatically creating a multi-paragraph overview article that provides a comprehensive summary of a subject of interest. Examples of such overviews include ac- tor biographies from IMDB and disease synopses from Wikipedia. Producing these texts by hand is a labor-intensive task, especially when relevant in- formation is scattered throughout a wide range of Internet sources. Our goal is to automate this pro- cess. We aim to create an overview of a subject – e.g., 3-M Syndrome – by intelligently combining relevant excerpts from across the Internet. As a starting point, we can employ meth- ods developed for multi-document summarization. However, our task poses additional technical chal- lenges with respect to content planning. Gen- erating a well-rounded overview article requires proactive strategies to gather relevant material, such as searching the Internet. Moreover, the chal- lenge of maintaining output readability is mag- nified when creating a longer document that dis- cusses multiple topics. In our approach, we explore how the high- level structure of human-authored documents can be used to produce well-formed comprehensive overview articles. We select relevant material for an article using a domain-specific automatically generated content template. For example, a tem- plate for articles about diseases might contain di- agnosis, causes, symptoms, and treatment. Our system induces these templates by analyzing pat- terns in the structure of human-authored docu- ments in the domain of interest. Then, it produces a new article by selecting content from the Internet for each part of this template. An example of our system’s output1 is shown in Figure 1. The algorithmic innovation of our work is a method for learning topic-specific extractors for content selection jointly across the entire template. Learning a single topic-specific extractor can be easily achieved in a standard classification frame- work. However, the choices for different topics in a template are mutually dependent; for exam- ple, in a multi-topic article, there is potential for redundancy across topics. Simultaneously learn- …

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