In this paper, we investigate an approach for creating a comprehensive textual overview of a subject composed of information 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 innovation of our work is a method to learn topicspecific extractors for content selection jointly for the entire template. We 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 our evaluation confirm the benefits of incorporating structural information into the content selection process.

Full text: http://people.csail.mit.edu/csauper/pubs/sauper-acl-09.pdf Sample articles: http://people.csail.mit.edu/csauper/?page_id=64

This code is available for research use only.

Run options are available by running perceptron_ranker_full.py.

A sample data file is provided in data/sample.data. In general, for the section /Section Name/ in the file sections.section_name, the format is as follows:

##Article## !!section title!! body text ...