@InProceedings{peyrard-gurevych:2018:N18-2,
  author    = {Peyrard, Maxime  and  Gurevych, Iryna},
  title     = {Objective Function Learning to Match Human Judgements for Optimization-Based Summarization},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {654--660},
  abstract  = {Supervised summarization systems usually rely on supervision at the sentence or n-gram level provided by automatic metrics like ROUGE, which act as noisy proxies for human judgments. In this work, we learn a summary-level scoring function $\theta$ including human judgments as supervision and automatically generated data as regularization. We extract summaries with a genetic algorithm using $\theta$ as a fitness function. We observe strong and promising performances across datasets in both automatic and manual evaluation.},
  url       = {http://www.aclweb.org/anthology/N18-2103}
}