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Integrating Ontology-Based Knowledge to Improve Biomedical Multi-Document Summarization Model

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Intelligent Information and Database Systems (ACIIDS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13996))

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Abstract

Most existing extractive summarization models use the original text’s internal information and calculate each sentence’s importance individually. When applied to specific domains (such as verbal text, biomedical literature, etc.), these models have some drawbacks: the variety of synonym terms, unknown words or terminologies, and the intra-document and inter-document relations between sentences or terms. In this work, we proposed an ontology-based summarization model that leverages many knowledge bases to understand the input documents. Our proposed model was built with an integrated ontology and a signal transmission-based method for extending domain knowledge such as related terms, and relationships between terms and sentences. The proposed model has been proven effective with the highest ROUGE-2 F1 score in the test dataset of the MEDIQA 2021 MAS shared tasks.

Q.-A. Nguyen and K.-V. Nguyen—Contributed Equally.

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Notes

  1. 1.

    https://sites.google.com/view/mediqa2021.

  2. 2.

    https://disease-ontology.org/.

  3. 3.

    http://geneontology.org/.

  4. 4.

    https://www.ncbi.nlm.nih.gov/mesh/.

  5. 5.

    https://www.ncbi.nlm.nih.gov/mesh/.

  6. 6.

    https://mondo.monarchinitiative.org.

  7. 7.

    http://symptomontologywiki.igs.umaryland.edu.

  8. 8.

    http://ctdbase.org.

  9. 9.

    https://chiqa.nlm.nih.gov.

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Acknowledgements

This research has been done under the research project QG “Research and Development of Vietnamese Multi-document Summarization Based on Advanced Language Models” of Vietnam National University, Hanoi (Code: QG.22.61). Quoc-An Nguyen was funded by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2022.ThS.001.

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Authors and Affiliations

  1. University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam

    Quoc-An Nguyen, Khanh-Vinh Nguyen, Hoang Quynh Le, Duy-Cat Can, Tam Doan-Thanh & Mai-Vu Tran

  2. Viettel Group, Hanoi, Vietnam

    Tam Doan-Thanh

  3. Hanoi Medical University, Hanoi, Vietnam

    Trung-Hieu Do

Authors
  1. Quoc-An Nguyen
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  2. Khanh-Vinh Nguyen
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  3. Hoang Quynh Le
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  4. Duy-Cat Can
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  5. Tam Doan-Thanh
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  6. Trung-Hieu Do
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  7. Mai-Vu Tran
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Corresponding author

Correspondence to Mai-Vu Tran .

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Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Siridech Boonsang

  3. Iwate Prefectural University Iwate, Iwate, Japan

    Hamido Fujita

  4. Wroclaw University of Science and Technology, Wrocław, Poland

    Bogumiła Hnatkowska

  5. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  6. King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  7. Malaysia Japan International Institute of Technology, Kuala Lumpur, Malaysia

    Ali Selamat

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Nguyen, QA. et al. (2023). Integrating Ontology-Based Knowledge to Improve Biomedical Multi-Document Summarization Model. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2023. Lecture Notes in Computer Science(), vol 13996. Springer, Singapore. https://doi.org/10.1007/978-981-99-5837-5_9

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  • DOI: https://doi.org/10.1007/978-981-99-5837-5_9

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  • Online ISBN: 978-981-99-5837-5

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