@InProceedings{miller-mccoy:2017:FrontiersSummarization,
  author    = {Miller, John  and  McCoy, Kathleen},
  title     = {Topic Model Stability for Hierarchical Summarization},
  booktitle = {Proceedings of the Workshop on New Frontiers in Summarization},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {64--73},
  abstract  = {We envisioned responsive generic hierarchical text summarization with summaries
	organized by section and paragraph based on hierarchical structure topic
	models. But we had to be sure that topic models were stable for the sampled
	corpora. To that end we developed a methodology for aligning multiple
	hierarchical structure topic models run over the same corpus under similar
	conditions, calculating a representative centroid model, and reporting
	stability of the centroid model. We ran stability experiments for standard
	corpora and a development corpus of Global Warming articles. We found flat and
	hierarchical structures of two levels plus the root offer stable centroid
	models, but hierarchical structures of three levels plus the root didn't seem
	stable enough for use in hierarchical summarization.},
  url       = {http://www.aclweb.org/anthology/W17-4509}
}