@InProceedings{peyrard-ecklekohler:2017:Long,
  author    = {Peyrard, Maxime  and  Eckle-Kohler, Judith},
  title     = {Supervised Learning of Automatic Pyramid for Optimization-Based Multi-Document Summarization},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {1084--1094},
  abstract  = {We present a  new supervised framework that learns to estimate automatic
	Pyramid scores and uses them for optimization-based extractive multi-document
	summarization. For learning automatic Pyramid scores, we developed a method for
	automatic training data generation which is based on a genetic algorithm using
	automatic Pyramid as the fitness function. Our experimental evaluation shows
	that  our new framework significantly outperforms strong baselines regarding
	automatic Pyramid, and that there is much room for improvement in comparison
	with the upper-bound for automatic Pyramid.},
  url       = {http://aclweb.org/anthology/P17-1100}
}