<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nair-singh-2021-reducing-repetition">
    <titleInfo>
        <title>On Reducing Repetition in Abstractive Summarization</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pranav</namePart>
        <namePart type="family">Nair</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anil</namePart>
        <namePart type="given">Kumar</namePart>
        <namePart type="family">Singh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Student Research Workshop Associated with RANLP 2021</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Souhila</namePart>
            <namePart type="family">Djabri</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dinara</namePart>
            <namePart type="family">Gimadi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tsvetomila</namePart>
            <namePart type="family">Mihaylova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ivelina</namePart>
            <namePart type="family">Nikolova-Koleva</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Repetition in natural language generation reduces the informativeness of text and makes it less appealing. Various techniques have been proposed to alleviate it. In this work, we explore and propose techniques to reduce repetition in abstractive summarization. First, we explore the application of unlikelihood training and embedding matrix regularizers from previous work on language modeling to abstractive summarization. Next, we extend the coverage and temporal attention mechanisms to the token level to reduce repetition. In our experiments on the CNN/Daily Mail dataset, we observe that these techniques reduce the amount of repetition and increase the informativeness of the summaries, which we confirm via human evaluation.</abstract>
    <identifier type="citekey">nair-singh-2021-reducing-repetition</identifier>
    <location>
        <url>https://aclanthology.org/2021.ranlp-srw.18</url>
    </location>
    <part>
        <date>2021-09</date>
        <extent unit="page">
            <start>126</start>
            <end>134</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T On Reducing Repetition in Abstractive Summarization
%A Nair, Pranav
%A Singh, Anil Kumar
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F nair-singh-2021-reducing-repetition
%X Repetition in natural language generation reduces the informativeness of text and makes it less appealing. Various techniques have been proposed to alleviate it. In this work, we explore and propose techniques to reduce repetition in abstractive summarization. First, we explore the application of unlikelihood training and embedding matrix regularizers from previous work on language modeling to abstractive summarization. Next, we extend the coverage and temporal attention mechanisms to the token level to reduce repetition. In our experiments on the CNN/Daily Mail dataset, we observe that these techniques reduce the amount of repetition and increase the informativeness of the summaries, which we confirm via human evaluation.
%U https://aclanthology.org/2021.ranlp-srw.18
%P 126-134

Markdown (Informal)

[On Reducing Repetition in Abstractive Summarization](https://aclanthology.org/2021.ranlp-srw.18) (Nair & Singh, RANLP 2021)

• On Reducing Repetition in Abstractive Summarization (Nair & Singh, RANLP 2021)

ACL

• Pranav Nair and Anil Kumar Singh. 2021. On Reducing Repetition in Abstractive Summarization. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 126–134, Online. INCOMA Ltd..