<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gong-etal-2018-document">
    <titleInfo>
        <title>Document Similarity for Texts of Varying Lengths via Hidden Topics</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hongyu</namePart>
        <namePart type="family">Gong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tarek</namePart>
        <namePart type="family">Sakakini</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Suma</namePart>
        <namePart type="family">Bhat</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">JinJun</namePart>
        <namePart type="family">Xiong</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of the incorporation of domain knowledge to text matching.</abstract>
    <identifier type="citekey">gong-etal-2018-document</identifier>
    <identifier type="doi">10.18653/v1/P18-1218</identifier>
    <location>
        <url>https://aclanthology.org/P18-1218</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>2341</start>
            <end>2351</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Document Similarity for Texts of Varying Lengths via Hidden Topics
%A Gong, Hongyu
%A Sakakini, Tarek
%A Bhat, Suma
%A Xiong, JinJun
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F gong-etal-2018-document
%X Measuring similarity between texts is an important task for several applications. Available approaches to measure document similarity are inadequate for document pairs that have non-comparable lengths, such as a long document and its summary. This is because of the lexical, contextual and the abstraction gaps between a long document of rich details and its concise summary of abstract information. In this paper, we present a document matching approach to bridge this gap, by comparing the texts in a common space of hidden topics. We evaluate the matching algorithm on two matching tasks and find that it consistently and widely outperforms strong baselines. We also highlight the benefits of the incorporation of domain knowledge to text matching.
%R 10.18653/v1/P18-1218
%U https://aclanthology.org/P18-1218
%U https://doi.org/10.18653/v1/P18-1218
%P 2341-2351

Markdown (Informal)

[Document Similarity for Texts of Varying Lengths via Hidden Topics](https://aclanthology.org/P18-1218) (Gong et al., ACL 2018)

• Document Similarity for Texts of Varying Lengths via Hidden Topics (Gong et al., ACL 2018)

ACL

• Hongyu Gong, Tarek Sakakini, Suma Bhat, and JinJun Xiong. 2018. Document Similarity for Texts of Varying Lengths via Hidden Topics. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2341–2351, Melbourne, Australia. Association for Computational Linguistics.