<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="li-etal-2019-keep">
    <titleInfo>
        <title>Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Manling</namePart>
        <namePart type="family">Li</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lingyu</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Heng</namePart>
        <namePart type="family">Ji</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Richard</namePart>
        <namePart type="given">J</namePart>
        <namePart type="family">Radke</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention across three levels: segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional text features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that the utterance is more important if the speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.</abstract>
    <identifier type="citekey">li-etal-2019-keep</identifier>
    <identifier type="doi">10.18653/v1/P19-1210</identifier>
    <location>
        <url>https://aclanthology.org/P19-1210</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>2190</start>
            <end>2196</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization
%A Li, Manling
%A Zhang, Lingyu
%A Ji, Heng
%A Radke, Richard J.
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F li-etal-2019-keep
%X Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention across three levels: segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional text features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that the utterance is more important if the speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.
%R 10.18653/v1/P19-1210
%U https://aclanthology.org/P19-1210
%U https://doi.org/10.18653/v1/P19-1210
%P 2190-2196

Markdown (Informal)

[Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization](https://aclanthology.org/P19-1210) (Li et al., ACL 2019)

• Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization (Li et al., ACL 2019)

ACL

• Manling Li, Lingyu Zhang, Heng Ji, and Richard J. Radke. 2019. Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2190–2196, Florence, Italy. Association for Computational Linguistics.