Pedagogically useful extractive summaries for science education

Authors:
Sebastian de la Chica;Faisal Ahmad;James H. Martin;Tamara Sumner
Affiliations:
University of Colorado at Boulder;University of Colorado at Boulder;University of Colorado at Boulder;University of Colorado at Boulder
Venue:
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Year:
2008

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Abstract

This paper describes the design and evaluation of an extractive summarizer for educational science content called COGENT. COGENT extends MEAD based on strategies elicited from an empirical study with science domain and instructional design experts. COGENT identifies sentences containing pedagogically relevant concepts for a specific science domain. The algorithms pursue a hybrid approach integrating both domain independent bottom-up sentence scoring features and domain-aware top-down features. Evaluation results indicate that COGENT outperforms existing summarizers and generates summaries that closely resemble those generated by human experts. COGENT concept inventories appear to also support the computational identification of student misconceptions about earthquakes and plate tectonics.