@InProceedings{he-li-zhuge:2016:COLING,
  author    = {He, Lei  and  Li, Wei  and  Zhuge, Hai},
  title     = {Exploring Differential Topic Models for Comparative Summarization of Scientific Papers},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {1028--1038},
  abstract  = {This paper investigates differential topic models (dTM) for summarizing the
	differences among document groups. Starting from a simple probabilistic
	generative model, we propose dTM-SAGE that explicitly models the deviations on
	group-specific word distributions to indicate how words are used
	differen-tially across different document groups from a background word
	distribution. It is more effective to capture unique characteristics for
	comparing document groups. To generate dTM-based comparative summaries, we
	propose two sentence scoring methods for measuring the sentence discriminative
	capacity. Experimental results on scientific papers dataset show that our
	dTM-based comparative summari-zation methods significantly outperform the
	generic baselines and the state-of-the-art comparative summarization methods
	under ROUGE metrics.},
  url       = {http://aclweb.org/anthology/C16-1098}
}