@InProceedings{wu-chang-chen:2017:I17-4,
  author    = {Wu, Shih-Hung  and  Chang, Su-Yu  and  Chen, Liang-Pu},
  title     = {CYUT at IJCNLP-2017 Task 3: System Report for Review Opinion Diversification},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {134--137},
  abstract  = {Review Opinion Diversification (RevOpiD) 2017 is a shared task which is held in
	International Joint Conference on Natural Language Processing (IJCNLP). The
	shared task aims at selecting top-k reviews, as a summary, from a set of
	re-views. There are three subtasks in RevOpiD: helpfulness ranking,
	rep-resentativeness ranking, and ex-haustive coverage ranking. This year, our
	team submitted runs by three models. We focus on ranking reviews based on the
	helpfulness of the reviews. In the first two models, we use linear regression
	with two different loss functions. First one is least squares, and second one
	is cross entropy. The third run is a random baseline. For both k=5 and k=10,
	our second model gets the best scores in the official evaluation metrics.},
  url       = {http://www.aclweb.org/anthology/I17-4022}
}