<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="rohanian-2017-multi">
    <titleInfo>
        <title>Multi-Document Summarization of Persian Text using Paragraph Vectors</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Morteza</namePart>
        <namePart type="family">Rohanian</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Student Research Workshop Associated with RANLP 2017</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Venelin</namePart>
            <namePart type="family">Kovatchev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Irina</namePart>
            <namePart type="family">Temnikova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pepa</namePart>
            <namePart type="family">Gencheva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yasen</namePart>
            <namePart type="family">Kiprov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Varna</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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>
    <identifier type="citekey">rohanian-2017-multi</identifier>
    <identifier type="doi">10.26615/issn.1314-9156.2017_005</identifier>
    <location>
        <url>https://aclanthology.org/R17-2005/</url>
    </location>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>35</start>
            <end>40</end>
        </extent>
    </part>
</mods>
</modsCollection>

%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://aclanthology.org/R17-2005/
%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://aclanthology.org/R17-2005/) (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..