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    <titleInfo>
        <title>Transformer-based Models for Long Document Summarisation in Financial Domain</title>
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    <name type="personal">
        <namePart type="given">Urvashi</namePart>
        <namePart type="family">Khanna</namePart>
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    <name type="personal">
        <namePart type="given">Samira</namePart>
        <namePart type="family">Ghodratnama</namePart>
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        <namePart type="given">Diego</namePart>
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    <name type="personal">
        <namePart type="given">Amin</namePart>
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            <title>Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022</title>
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            <namePart type="given">Mahmoud</namePart>
            <namePart type="family">El-Haj</namePart>
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            <namePart type="given">Paul</namePart>
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    <abstract>Summarisation of long financial documents is a challenging task due to the lack of large-scale datasets and the need for domain knowledge experts to create human-written summaries. Traditional summarisation approaches that generate a summary based on the content cannot produce summaries comparable to human-written ones and thus are rarely used in practice. In this work, we use the Longformer-Encoder-Decoder (LED) model to handle long financial reports. We describe our experiments and participating systems in the financial narrative summarisation shared task. Multi-stage fine-tuning helps the model generalise better on niche domains and avoids the problem of catastrophic forgetting. We further investigate the effect of the staged fine-tuning approach on the FNS dataset. Our systems achieved promising results in terms of ROUGE scores on the validation dataset.</abstract>
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        <date>2022-06</date>
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            <start>73</start>
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%0 Conference Proceedings
%T Transformer-based Models for Long Document Summarisation in Financial Domain
%A Khanna, Urvashi
%A Ghodratnama, Samira
%A Moll ́a, Diego
%A Beheshti, Amin
%Y El-Haj, Mahmoud
%Y Rayson, Paul
%Y Zmandar, Nadhem
%S Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F khanna-etal-2022-transformer
%X Summarisation of long financial documents is a challenging task due to the lack of large-scale datasets and the need for domain knowledge experts to create human-written summaries. Traditional summarisation approaches that generate a summary based on the content cannot produce summaries comparable to human-written ones and thus are rarely used in practice. In this work, we use the Longformer-Encoder-Decoder (LED) model to handle long financial reports. We describe our experiments and participating systems in the financial narrative summarisation shared task. Multi-stage fine-tuning helps the model generalise better on niche domains and avoids the problem of catastrophic forgetting. We further investigate the effect of the staged fine-tuning approach on the FNS dataset. Our systems achieved promising results in terms of ROUGE scores on the validation dataset.
%U https://aclanthology.org/2022.fnp-1.10
%P 73-78

Markdown (Informal)

[Transformer-based Models for Long Document Summarisation in Financial Domain](https://aclanthology.org/2022.fnp-1.10) (Khanna et al., FNP 2022)

• Transformer-based Models for Long Document Summarisation in Financial Domain (Khanna et al., FNP 2022)

ACL

• Urvashi Khanna, Samira Ghodratnama, Diego Moll ́a, and Amin Beheshti. 2022. Transformer-based Models for Long Document Summarisation in Financial Domain. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 73–78, Marseille, France. European Language Resources Association.