@InProceedings{rohanian:2017:RANLPStud,
  author    = {Rohanian, Morteza},
  title     = {Multi-Document Summarization of Persian Text using Paragraph Vectors},
  booktitle = {Proceedings of the Student Research Workshop Associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna},
  publisher = {INCOMA Ltd.},
  pages     = {35--40},
  abstract  = {A multi-document summarizer finds the key topics from multiple textual sources
	and organizes information around them. In this paper we propose a summarization
	method for Persian text using paragraph vectors that can represent textual
	units of arbitrary lengths. We use these vectors to calculate the semantic
	relatedness between documents, cluster them to a number of predetermined
	groups, weight them based on their distance to the centroids and the
	intra-cluster homogeneity and take out the key paragraphs. We compare the final
	summaries with the gold-standard summaries of 21 digital topics using the ROUGE
	evaluation metric. Experimental results show the advantages of using paragraph
	vectors over earlier attempts at developing similar methods for a low resource
	language like Persian.},
  url       = {https://doi.org/10.26615/issn.1314-9156.2017_005}
}