@InProceedings{zopf-lozamencia-furnkranz:2016:COLING,
  author    = {Zopf, Markus  and  Loza Menc\'{i}a, Eneldo  and  F\"{u}rnkranz, Johannes},
  title     = {Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization},
  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     = {1071--1082},
  abstract  = {Unexpected events such as accidents, natural disasters and terrorist attacks
	represent an information situation where it is crucial to give users access to
	important and non-redundant information as early as possible. Incremental
	update summarization (IUS) aims at summarizing events which develop over time.
	In this paper, we propose a combination of sequential clustering and contextual
	importance measures to identify important sentences in a stream of documents in
	a timely manner. Sequential clustering is used to cluster similar sentences.
	The created clusters are scored by a contextual importance measure which
	identifies important information as well as redundant information. Experiments
	on the TREC Temporal Summarization 2015 shared task dataset show that our
	system achieves superior results compared to the best participating systems.},
  url       = {http://aclweb.org/anthology/C16-1102}
}