Automatic assessment of student reading comprehension from short summaries
Lisa Mintz, Dan C. Stefanescu, Shi Feng, Sidney K. D’Mello, Arthur C. Graesser · 2014
This paper describes our research on automatically scoring students ’ summaries for comprehension using not only text specific quantitative and qualitative features, but also more complex features based on the computational indices of cohesion available via Coh-Metrix and on Information Content (IC, a measure of text informativeness). We assessed whether human rated summary scores could be predicted by indices of text complexity and IC. The IC metric of the summaries was a better predictor of human scores than word count or any of the Coh-Metrix text complexity dimensions. This finding may justify the implementation of IC in future automated summary rating tools to rate short summaries.