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    <titleInfo>
        <title>A Dataset for Telling the Stories of Social Media Videos</title>
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    <name type="personal">
        <namePart type="given">Spandana</namePart>
        <namePart type="family">Gella</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">David</namePart>
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    <abstract>Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.</abstract>
    <identifier type="citekey">gella-etal-2018-dataset</identifier>
    <identifier type="doi">10.18653/v1/D18-1117</identifier>
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        <date>2018-oct-nov</date>
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            <start>968</start>
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%0 Conference Proceedings
%T A Dataset for Telling the Stories of Social Media Videos
%A Gella, Spandana
%A Lewis, Mike
%A Rohrbach, Marcus
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F gella-etal-2018-dataset
%X Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication. Automatically telling the stories using multi-sentence descriptions of videos would allow bridging this gap. To learn and evaluate such models, we introduce VideoStory a new large-scale dataset for video description as a new challenge for multi-sentence video description. Our VideoStory captions dataset is complementary to prior work and contains 20k videos posted publicly on a social media platform amounting to 396 hours of video with 123k sentences, temporally aligned to the video.
%R 10.18653/v1/D18-1117
%U https://aclanthology.org/D18-1117
%U https://doi.org/10.18653/v1/D18-1117
%P 968-974

Markdown (Informal)

[A Dataset for Telling the Stories of Social Media Videos](https://aclanthology.org/D18-1117) (Gella et al., EMNLP 2018)

• A Dataset for Telling the Stories of Social Media Videos (Gella et al., EMNLP 2018)

ACL

• Spandana Gella, Mike Lewis, and Marcus Rohrbach. 2018. A Dataset for Telling the Stories of Social Media Videos. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 968–974, Brussels, Belgium. Association for Computational Linguistics.