<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="an-etal-2023-vkie">
    <titleInfo>
        <title>VKIE: The Application of Key Information Extraction on Video Text</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Siyu</namePart>
        <namePart type="family">An</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ye</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Haoyuan</namePart>
        <namePart type="family">Peng</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Di</namePart>
        <namePart type="family">Yin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Mingxuan</namePart>
            <namePart type="family">Wang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Imed</namePart>
            <namePart type="family">Zitouni</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Extracting structured information from videos is critical for numerous downstream applications in the industry. In this paper, we define a significant task of extracting hierarchical key information from visual texts on videos. To fulfill this task, we decouple it into four subtasks and introduce two implementation solutions called PipVKIE and UniVKIE. PipVKIE sequentially completes the four subtasks in continuous stages, while UniVKIE is improved by unifying all the subtasks into one backbone. Both PipVKIE and UniVKIE leverage multimodal information from vision, text, and coordinates for feature representation. Extensive experiments on one well-defined dataset demonstrate that our solutions can achieve remarkable performance and efficient inference speed.</abstract>
    <identifier type="citekey">an-etal-2023-vkie</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-industry.51</identifier>
    <location>
        <url>https://aclanthology.org/2023.emnlp-industry.51</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>532</start>
            <end>540</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T VKIE: The Application of Key Information Extraction on Video Text
%A An, Siyu
%A Liu, Ye
%A Peng, Haoyuan
%A Yin, Di
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F an-etal-2023-vkie
%X Extracting structured information from videos is critical for numerous downstream applications in the industry. In this paper, we define a significant task of extracting hierarchical key information from visual texts on videos. To fulfill this task, we decouple it into four subtasks and introduce two implementation solutions called PipVKIE and UniVKIE. PipVKIE sequentially completes the four subtasks in continuous stages, while UniVKIE is improved by unifying all the subtasks into one backbone. Both PipVKIE and UniVKIE leverage multimodal information from vision, text, and coordinates for feature representation. Extensive experiments on one well-defined dataset demonstrate that our solutions can achieve remarkable performance and efficient inference speed.
%R 10.18653/v1/2023.emnlp-industry.51
%U https://aclanthology.org/2023.emnlp-industry.51
%U https://doi.org/10.18653/v1/2023.emnlp-industry.51
%P 532-540

Markdown (Informal)

[VKIE: The Application of Key Information Extraction on Video Text](https://aclanthology.org/2023.emnlp-industry.51) (An et al., EMNLP 2023)

• VKIE: The Application of Key Information Extraction on Video Text (An et al., EMNLP 2023)

ACL

• Siyu An, Ye Liu, Haoyuan Peng, and Di Yin. 2023. VKIE: The Application of Key Information Extraction on Video Text. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 532–540, Singapore. Association for Computational Linguistics.