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Node-Weighted Centrality Ranking for Unsupervised Long Document Summarization

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Natural Language Processing and Information Systems (NLDB 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13913))

Abstract

Supervised methods have demonstrated superior performance to unsupervised methods in text summarization. However, supervised methods heavily rely on human-generated summaries, which can be costly and difficult to obtain in large quantities. They also face challenges in summarizing long documents due to input length restrictions. Graph-based methods are frequently employed in unsupervised text summarization owing to their capacity to examine interrelationships between. However, these methods usually depend on unique node weights, resulting in limited mapping capabilities and weak performance on long documents. To address these difficulties, this study proposes an unsupervised method that employs a graph model with augmented node weights with a novel centrality ranking algorithm. Comprehensive experiments on standard datasets demonstrate the effectiveness of the proposed method, which outperforms both unsupervised and supervised techniques when evaluated using the ROUGE metric.

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Notes

  1. 1.

    https://pypi.org/project/sumy/.

  2. 2.

    https://huggingface.co/sentence-transformers/roberta-base-nli-stsb-mean-tokens.

  3. 3.

    https://pypi.org/project/py-rouge/.

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Acknowledgments

The first author would like to acknowledge the Ministry of National Education of Turkey for the financial support of her research activity.

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Authors and Affiliations

  1. University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK

    Tuba Gokhan, Phillip Smith & Mark Lee

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  1. Tuba Gokhan
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  2. Phillip Smith
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Correspondence to Tuba Gokhan .

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Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Derby, Derby, UK

    Farid Meziane

  3. Oakland University, Rochester, NY, USA

    Vijayan Sugumaran

  4. University of Derby, Derby, UK

    Warren Manning

  5. University of Derby, Derby, UK

    Stephan Reiff-Marganiec

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Gokhan, T., Smith, P., Lee, M. (2023). Node-Weighted Centrality Ranking for Unsupervised Long Document Summarization. In: Métais, E., Meziane, F., Sugumaran, V., Manning, W., Reiff-Marganiec, S. (eds) Natural Language Processing and Information Systems. NLDB 2023. Lecture Notes in Computer Science, vol 13913. Springer, Cham. https://doi.org/10.1007/978-3-031-35320-8_21

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  • DOI: https://doi.org/10.1007/978-3-031-35320-8_21

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