@InProceedings{falke-meyer-gurevych:2017:I17-1,
  author    = {Falke, Tobias  and  Meyer, Christian M.  and  Gurevych, Iryna},
  title     = {Concept-Map-Based Multi-Document Summarization using Concept Coreference Resolution and Global Importance Optimization},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {801--811},
  abstract  = {Concept-map-based multi-document summarization is a variant of traditional
	summarization that produces structured summaries in the form of concept maps.
	In this work, we propose a new model for the task that addresses several issues
	in previous methods. It learns to identify and merge coreferent concepts to
	reduce redundancy, determines their importance with a strong supervised model
	and finds an optimal summary concept map via integer linear programming. It is
	also computationally more efficient than previous methods, allowing us to
	summarize larger document sets. We evaluate the model on two datasets, finding
	that it outperforms several approaches from previous work.},
  url       = {http://www.aclweb.org/anthology/I17-1081}
}