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        <namePart type="given">Mateusz</namePart>
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            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
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    <abstract>In recent years, the pattern of news consumption has been changing. The most popular multimedia news formats are now multimodal - the reader is often presented not only with a textual article but also with a short, vivid video. To draw the attention of the reader, such video-based articles are usually presented as a short textual summary paired with an image thumbnail. In this paper, we introduce MLASK (MultimodaL Article Summarization Kit) - a new dataset of video-based news articles paired with a textual summary and a cover picture, all obtained by automatically crawling several news websites. We demonstrate how the proposed dataset can be used to model the task of multimodal summarization by training a Transformer-based neural model. We also examine the effects of pre-training when the usage of generative pre-trained language models helps to improve the model performance, but (additional) pre-training on the simpler task of text summarization yields even better results. Our experiments suggest that the benefits of pre-training and using additional modalities in the input are not orthogonal.</abstract>
    <identifier type="citekey">krubinski-pecina-2023-mlask</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-eacl.67</identifier>
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        <date>2023-05</date>
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            <start>910</start>
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%0 Conference Proceedings
%T MLASK: Multimodal Summarization of Video-based News Articles
%A Krubiński, Mateusz
%A Pecina, Pavel
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F krubinski-pecina-2023-mlask
%X In recent years, the pattern of news consumption has been changing. The most popular multimedia news formats are now multimodal - the reader is often presented not only with a textual article but also with a short, vivid video. To draw the attention of the reader, such video-based articles are usually presented as a short textual summary paired with an image thumbnail. In this paper, we introduce MLASK (MultimodaL Article Summarization Kit) - a new dataset of video-based news articles paired with a textual summary and a cover picture, all obtained by automatically crawling several news websites. We demonstrate how the proposed dataset can be used to model the task of multimodal summarization by training a Transformer-based neural model. We also examine the effects of pre-training when the usage of generative pre-trained language models helps to improve the model performance, but (additional) pre-training on the simpler task of text summarization yields even better results. Our experiments suggest that the benefits of pre-training and using additional modalities in the input are not orthogonal.
%R 10.18653/v1/2023.findings-eacl.67
%U https://aclanthology.org/2023.findings-eacl.67
%U https://doi.org/10.18653/v1/2023.findings-eacl.67
%P 910-924

Markdown (Informal)

[MLASK: Multimodal Summarization of Video-based News Articles](https://aclanthology.org/2023.findings-eacl.67) (Krubiński & Pecina, Findings 2023)

• MLASK: Multimodal Summarization of Video-based News Articles (Krubiński & Pecina, Findings 2023)

ACL

• Mateusz Krubiński and Pavel Pecina. 2023. MLASK: Multimodal Summarization of Video-based News Articles. In Findings of the Association for Computational Linguistics: EACL 2023, pages 910–924, Dubrovnik, Croatia. Association for Computational Linguistics.