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        <title>Automatic Data Retrieval for Cross Lingual Summarization</title>
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    <name type="personal">
        <namePart type="given">Nikhilesh</namePart>
        <namePart type="family">Bhatnagar</namePart>
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    <name type="personal">
        <namePart type="given">Ashok</namePart>
        <namePart type="family">Urlana</namePart>
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    <name type="personal">
        <namePart type="given">Pruthwik</namePart>
        <namePart type="family">Mishra</namePart>
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    <name type="personal">
        <namePart type="given">Vandan</namePart>
        <namePart type="family">Mujadia</namePart>
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    <name type="personal">
        <namePart type="given">Dipti</namePart>
        <namePart type="family">M. Sharma</namePart>
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        <dateIssued>2023-12</dateIssued>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Jyoti</namePart>
            <namePart type="family">D. Pawar</namePart>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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            <publisher>NLP Association of India (NLPAI)</publisher>
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    <abstract>Cross-lingual summarization involves the sum marization of text written in one language to a different one. There is a body of research addressing cross-lingual summarization from English to other European languages. In this work, we aim to perform cross-lingual summarization from English to Hindi. We propose pairing up the coverage of newsworthy events in textual and video format can prove to be helpful for data acquisition for cross lingual summarization. We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs. We also outline filtering methods over reasonable thresholds to ensure the correctness of the summaries. Further, we make available 28,583 mono and cross-lingual article-summary pairs* . We also build and analyze multiple baselines on the collected data and report error analysis.</abstract>
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        <url>https://aclanthology.org/2023.icon-1.85/</url>
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        <date>2023-12</date>
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            <start>822</start>
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%0 Conference Proceedings
%T Automatic Data Retrieval for Cross Lingual Summarization
%A Bhatnagar, Nikhilesh
%A Urlana, Ashok
%A Mishra, Pruthwik
%A Mujadia, Vandan
%A M. Sharma, Dipti
%Y D. Pawar, Jyoti
%Y Lalitha Devi, Sobha
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F nikhilesh-etal-2023-automatic
%X Cross-lingual summarization involves the sum marization of text written in one language to a different one. There is a body of research addressing cross-lingual summarization from English to other European languages. In this work, we aim to perform cross-lingual summarization from English to Hindi. We propose pairing up the coverage of newsworthy events in textual and video format can prove to be helpful for data acquisition for cross lingual summarization. We analyze the data and propose methods to match articles to video descriptions that serve as document and summary pairs. We also outline filtering methods over reasonable thresholds to ensure the correctness of the summaries. Further, we make available 28,583 mono and cross-lingual article-summary pairs* . We also build and analyze multiple baselines on the collected data and report error analysis.
%U https://aclanthology.org/2023.icon-1.85/
%P 822-827

Markdown (Informal)

[Automatic Data Retrieval for Cross Lingual Summarization](https://aclanthology.org/2023.icon-1.85/) (Bhatnagar et al., ICON 2023)

• Automatic Data Retrieval for Cross Lingual Summarization (Bhatnagar et al., ICON 2023)

ACL

• Nikhilesh Bhatnagar, Ashok Urlana, Pruthwik Mishra, Vandan Mujadia, and Dipti M. Sharma. 2023. Automatic Data Retrieval for Cross Lingual Summarization. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 822–827, Goa University, Goa, India. NLP Association of India (NLPAI).