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        <title>Systematic TextRank Optimization in Extractive Summarization</title>
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        <namePart type="given">Morris</namePart>
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        <namePart type="given">Anthony</namePart>
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        <namePart type="given">Frederik</namePart>
        <namePart type="given">Juul</namePart>
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    <name type="personal">
        <namePart type="given">Ellis</namePart>
        <namePart type="given">Marie</namePart>
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    <name type="personal">
        <namePart type="given">Benyamin</namePart>
        <namePart type="family">Ahmadnia</namePart>
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            <title>Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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            <publisher>INCOMA Ltd., Shoumen, Bulgaria</publisher>
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    <abstract>With the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information. The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task. In this paper, we aim to optimize the performance of TextRank by systematically exploring and verifying the best preprocessing and fine-tuning techniques. We extensively evaluate text preprocessing methods, such as tokenization, stemming, and stopword removal, to identify the most effective combination with TextRank. Additionally, we examine fine-tuning strategies, including parameter optimization and incorporation of domain-specific knowledge, to achieve superior summarization quality.</abstract>
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        <date>2023-09</date>
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%0 Conference Proceedings
%T Systematic TextRank Optimization in Extractive Summarization
%A Zieve, Morris
%A Gregor, Anthony
%A Stokbaek, Frederik Juul
%A Lewis, Hunter
%A Mendoza, Ellis Marie
%A Ahmadnia, Benyamin
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F zieve-etal-2023-systematic
%X With the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information. The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task. In this paper, we aim to optimize the performance of TextRank by systematically exploring and verifying the best preprocessing and fine-tuning techniques. We extensively evaluate text preprocessing methods, such as tokenization, stemming, and stopword removal, to identify the most effective combination with TextRank. Additionally, we examine fine-tuning strategies, including parameter optimization and incorporation of domain-specific knowledge, to achieve superior summarization quality.
%U https://aclanthology.org/2023.ranlp-1.135
%P 1274-1281

Markdown (Informal)

[Systematic TextRank Optimization in Extractive Summarization](https://aclanthology.org/2023.ranlp-1.135) (Zieve et al., RANLP 2023)

• Systematic TextRank Optimization in Extractive Summarization (Zieve et al., RANLP 2023)

ACL

• Morris Zieve, Anthony Gregor, Frederik Juul Stokbaek, Hunter Lewis, Ellis Marie Mendoza, and Benyamin Ahmadnia. 2023. Systematic TextRank Optimization in Extractive Summarization. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 1274–1281, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.