<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="balepur-etal-2023-dynamite">
    <titleInfo>
        <title>DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Nishant</namePart>
        <namePart type="family">Balepur</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shivam</namePart>
        <namePart type="family">Agarwal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Karthik</namePart>
        <namePart type="family">Venkat Ramanan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Susik</namePart>
        <namePart type="family">Yoon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Diyi</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jiawei</namePart>
        <namePart type="family">Han</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: ACL 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rogers</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jordan</namePart>
            <namePart type="family">Boyd-Graber</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Naoaki</namePart>
            <namePart type="family">Okazaki</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Dynamic topic models (DTMs) analyze text streams to capture the evolution of topics. Despite their popularity, existing DTMs are either fully supervised, requiring expensive human annotations, or fully unsupervised, producing topic evolutions that often do not cater to a user’s needs. Further, the topic evolutions produced by DTMs tend to contain generic terms that are not indicative of their designated time steps. To address these issues, we propose the task of discriminative dynamic topic discovery. This task aims to discover topic evolutions from temporal corpora that distinctly align with a set of user-provided category names and uniquely capture topics at each time step. We solve this task by developing DynaMiTE, a framework that ensembles semantic similarity, category indicative, and time indicative scores to produce informative topic evolutions. Through experiments on three diverse datasets, including the use of a newly-designed human evaluation experiment, we demonstrate that DynaMiTE is a practical and efficient framework for helping users discover high-quality topic evolutions suited to their interests.</abstract>
    <identifier type="citekey">balepur-etal-2023-dynamite</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-acl.14</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-acl.14</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>194</start>
            <end>217</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance
%A Balepur, Nishant
%A Agarwal, Shivam
%A Venkat Ramanan, Karthik
%A Yoon, Susik
%A Yang, Diyi
%A Han, Jiawei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F balepur-etal-2023-dynamite
%X Dynamic topic models (DTMs) analyze text streams to capture the evolution of topics. Despite their popularity, existing DTMs are either fully supervised, requiring expensive human annotations, or fully unsupervised, producing topic evolutions that often do not cater to a user’s needs. Further, the topic evolutions produced by DTMs tend to contain generic terms that are not indicative of their designated time steps. To address these issues, we propose the task of discriminative dynamic topic discovery. This task aims to discover topic evolutions from temporal corpora that distinctly align with a set of user-provided category names and uniquely capture topics at each time step. We solve this task by developing DynaMiTE, a framework that ensembles semantic similarity, category indicative, and time indicative scores to produce informative topic evolutions. Through experiments on three diverse datasets, including the use of a newly-designed human evaluation experiment, we demonstrate that DynaMiTE is a practical and efficient framework for helping users discover high-quality topic evolutions suited to their interests.
%R 10.18653/v1/2023.findings-acl.14
%U https://aclanthology.org/2023.findings-acl.14
%U https://doi.org/10.18653/v1/2023.findings-acl.14
%P 194-217

Markdown (Informal)

[DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance](https://aclanthology.org/2023.findings-acl.14) (Balepur et al., Findings 2023)

• DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance (Balepur et al., Findings 2023)

ACL

• Nishant Balepur, Shivam Agarwal, Karthik Venkat Ramanan, Susik Yoon, Diyi Yang, and Jiawei Han. 2023. DynaMiTE: Discovering Explosive Topic Evolutions with User Guidance. In Findings of the Association for Computational Linguistics: ACL 2023, pages 194–217, Toronto, Canada. Association for Computational Linguistics.