@InProceedings{rossiello-basile-semeraro:2017:MultiLing2017,
  author    = {Rossiello, Gaetano  and  Basile, Pierpaolo  and  Semeraro, Giovanni},
  title     = {Centroid-based Text Summarization through Compositionality of Word Embeddings},
  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     = {12--21},
  abstract  = {The textual similarity is a crucial aspect for many extractive text
	summarization methods. A bag-of-words representation does not allow to grasp
	the semantic relationships between concepts when comparing strongly related
	sentences with no words in common. To overcome this issue, in this paper we
	propose a centroid-based method for text summarization that exploits the
	compositional capabilities of word embeddings. The evaluations on
	multi-document and multilingual datasets prove the effectiveness of the
	continuous vector representation of words compared to the bag-of-words model.
	Despite its simplicity, our method achieves good performance even in comparison
	to more complex deep learning models. Our method is unsupervised and it can be
	adopted in other summarization tasks.},
  url       = {http://www.aclweb.org/anthology/W17-1003}
}