<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="peyrard-eckle-kohler-2016-general">
    <titleInfo>
        <title>A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Maxime</namePart>
        <namePart type="family">Peyrard</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Judith</namePart>
        <namePart type="family">Eckle-Kohler</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2016-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>The COLING 2016 Organizing Committee</publisher>
            <place>
                <placeTerm type="text">Osaka, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers – one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.</abstract>
    <identifier type="citekey">peyrard-eckle-kohler-2016-general</identifier>
    <location>
        <url>https://aclanthology.org/C16-1024</url>
    </location>
    <part>
        <date>2016-12</date>
        <extent unit="page">
            <start>247</start>
            <end>257</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence
%A Peyrard, Maxime
%A Eckle-Kohler, Judith
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F peyrard-eckle-kohler-2016-general
%X Extracting summaries via integer linear programming and submodularity are popular and successful techniques in extractive multi-document summarization. However, many interesting optimization objectives are neither submodular nor factorizable into an integer linear program. We address this issue and present a general optimization framework where any function of input documents and a system summary can be plugged in. Our framework includes two kinds of summarizers – one based on genetic algorithms, the other using a swarm intelligence approach. In our experimental evaluation, we investigate the optimization of two information-theoretic summary evaluation metrics and find that our framework yields competitive results compared to several strong summarization baselines. Our comparative analysis of the genetic and swarm summarizers reveals interesting complementary properties.
%U https://aclanthology.org/C16-1024
%P 247-257

Markdown (Informal)

[A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence](https://aclanthology.org/C16-1024) (Peyrard & Eckle-Kohler, COLING 2016)

• A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence (Peyrard & Eckle-Kohler, COLING 2016)

ACL

• Maxime Peyrard and Judith Eckle-Kohler. 2016. A General Optimization Framework for Multi-Document Summarization Using Genetic Algorithms and Swarm Intelligence. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 247–257, Osaka, Japan. The COLING 2016 Organizing Committee.