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        <title>Multi-Document Summarization of Persian Text using Paragraph Vectors</title>
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        <namePart type="given">Morteza</namePart>
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            <title>Proceedings of the Student Research Workshop Associated with RANLP 2017</title>
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    <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.</abstract>
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    <identifier type="doi">10.26615/issn.1314-9156.2017_005</identifier>
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%0 Conference Proceedings
%T Multi-Document Summarization of Persian Text using Paragraph Vectors
%A Rohanian, Morteza
%S Proceedings of the Student Research Workshop Associated with RANLP 2017
%D 2017
%8 sep
%I INCOMA Ltd.
%C Varna
%F rohanian-2017-multi
%X 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.
%R 10.26615/issn.1314-9156.2017_005
%U https://doi.org/10.26615/issn.1314-9156.2017_005
%P 35-40

Markdown (Informal)

[Multi-Document Summarization of Persian Text using Paragraph Vectors](https://doi.org/10.26615/issn.1314-9156.2017_005) (Rohanian, RANLP 2017)

• Multi-Document Summarization of Persian Text using Paragraph Vectors (Rohanian, RANLP 2017)

ACL

• Morteza Rohanian. 2017. Multi-Document Summarization of Persian Text using Paragraph Vectors. In Proceedings of the Student Research Workshop Associated with RANLP 2017, pages 35–40, Varna. INCOMA Ltd..