An Ensemble method for Content Selection for Data-to-text Systems
This is an incremental improvement for generating student feedback from learning data.
The paper tackles automatic report generation from time-series data for student feedback by treating content selection as a multi-label classification problem, achieving higher accuracy and F-score compared to baselines.
We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) problem, which takes as input time-series data (students' learning data) and outputs a summary of these data (feedback). Unlike previous work, this method considers all data simultaneously using ensembles of classifiers, and therefore, it achieves higher accuracy and F- score compared to meaningful baselines.