@InProceedings{litvak-vanetik:2017:MultiLing2017,
  author    = {Litvak, Marina  and  Vanetik, Natalia},
  title     = {Query-based summarization using MDL principle},
  booktitle = {Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {22--31},
  abstract  = {Query-based text summarization is aimed at extracting essential information
	that answers the query from original text. The answer is presented  
	in a minimal, often predefined, number of words. In this paper we introduce a
	new unsupervised approach for query-based extractive summarization, based on
	the minimum description length (MDL) principle that employs Krimp compression
	algorithm (Vreeken et al., 2011). The key idea of our approach is to select
	frequent word sets related to a given query that compress document sentences
	better and therefore describe the document better.
	A summary is extracted by selecting sentences that best cover query-related
	frequent word sets.
	The approach is evaluated based on the DUC 2005 and DUC 2006 datasets which are
	specifically designed for query-based summarization (DUC, 2005 2006). It
	competes with the best results.},
  url       = {http://www.aclweb.org/anthology/W17-1004}
}