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        <title>Multi-Document Summarization of Persian Text using Paragraph Vectors</title>
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            <title>Proceedings of the Student Research Workshop Associated with RANLP 2017</title>
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            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
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            <namePart type="given">Irina</namePart>
            <namePart type="family">Temnikova</namePart>
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        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova</namePart>
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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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%0 Conference Proceedings
%T Multi-Document Summarization of Persian Text using Paragraph Vectors
%A Rohanian, Morteza
%Y Kovatchev, Venelin
%Y Temnikova, Irina
%Y Gencheva, Pepa
%Y Kiprov, Yasen
%Y Nikolova, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2017
%D 2017
%8 September
%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..