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.
@article{arxiv.1506.02922,
title = {An Ensemble method for Content Selection for Data-to-text Systems},
author = {Dimitra Gkatzia and Helen Hastie},
journal= {arXiv preprint arXiv:1506.02922},
year = {2015}
}
Comments
3 pages, 2 figures, 1st International Workshop on Data-to-text Generation