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        <title>swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain</title>
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    <name type="personal">
        <namePart type="given">Pierluigi</namePart>
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    <name type="personal">
        <namePart type="given">Cataldo</namePart>
        <namePart type="family">Musto</namePart>
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    <name type="personal">
        <namePart type="given">Marco</namePart>
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    <name type="personal">
        <namePart type="given">Georgios</namePart>
        <namePart type="family">Lekkas</namePart>
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    <name type="personal">
        <namePart type="given">Giovanni</namePart>
        <namePart type="family">Semeraro</namePart>
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            <title>Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022</title>
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        <name type="personal">
            <namePart type="given">Mahmoud</namePart>
            <namePart type="family">El-Haj</namePart>
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        <name type="personal">
            <namePart type="given">Paul</namePart>
            <namePart type="family">Rayson</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nadhem</namePart>
            <namePart type="family">Zmandar</namePart>
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    <abstract>In this paper, we introduce the results of our submitted system to the FinTOC 2022 task. We address the task using a two-stage process: first, we detect titles using Document Image Analysis, then we train a supervised model for the hierarchical level prediction. We perform Document Image Analysis using a pre-trained Faster R-CNN on the PublyaNet dataset. We fine-tuned the model on the FinTOC 2022 training set. We extract orthographic and layout features from detected titles and use them to train a Random Forest model to predict the title level. The proposed system ranked #1 on both Title Detection and the Table of Content extraction tasks for Spanish. The system ranked #3 on both the two subtasks for English and French.</abstract>
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        <url>https://aclanthology.org/2022.fnp-1.14</url>
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        <date>2022-06</date>
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            <start>95</start>
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%0 Conference Proceedings
%T swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain
%A Cassotti, Pierluigi
%A Musto, Cataldo
%A DeGemmis, Marco
%A Lekkas, Georgios
%A Semeraro, Giovanni
%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 cassotti-etal-2022-swapuniba
%X In this paper, we introduce the results of our submitted system to the FinTOC 2022 task. We address the task using a two-stage process: first, we detect titles using Document Image Analysis, then we train a supervised model for the hierarchical level prediction. We perform Document Image Analysis using a pre-trained Faster R-CNN on the PublyaNet dataset. We fine-tuned the model on the FinTOC 2022 training set. We extract orthographic and layout features from detected titles and use them to train a Random Forest model to predict the title level. The proposed system ranked #1 on both Title Detection and the Table of Content extraction tasks for Spanish. The system ranked #3 on both the two subtasks for English and French.
%U https://aclanthology.org/2022.fnp-1.14
%P 95-99

Markdown (Informal)

[swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain](https://aclanthology.org/2022.fnp-1.14) (Cassotti et al., FNP 2022)

• swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain (Cassotti et al., FNP 2022)

ACL

• Pierluigi Cassotti, Cataldo Musto, Marco DeGemmis, Georgios Lekkas, and Giovanni Semeraro. 2022. swapUNIBA@FinTOC2022: Fine-tuning Pre-trained Document Image Analysis Model for Title Detection on the Financial Domain. In Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, pages 95–99, Marseille, France. European Language Resources Association.