Record link: 
http://hdl.handle.net/20.500.12708/192520
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Title: 
A Platform for Argumentative Zoning Annotation and Scientific Summarization
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Citation: 
El-Ebshihy, A., Ningtyas, A. M., Andersson, L., Piroi, F., & Rauber, A. (2022). A Platform for Argumentative Zoning Annotation and Scientific Summarization. In M. A. Hasan & L. Xiong (Eds.), CIKM ’22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 4843–4847). Association for Computing Machinery (ACM). https://doi.org/10.1145/3511808.3557193
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Publisher DOI: 
10.1145/3511808.3557193
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
El-Ebshihy, Alaa 
Ningtyas, Annisa Maulida 
Andersson, Linda 
Piroi, Florina 
Rauber, Andreas 
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Organisational Unit: 
E194-04 - Forschungsbereich Data Science 
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Published in: 
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management 
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ISBN: 
978-1-4503-9236-5
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Date (published): 
Oct-2022
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Event name: 
CIKM '22: 31st ACM International Conference on Information & Knowledge Management 
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Event date: 
17-Oct-2022 - 21-Oct-2022
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Event place: 
Atlanta, United States of America (the)
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Number of Pages: 
5
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Publisher: 
Association for Computing Machinery (ACM), New York
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Peer reviewed: 
Yes
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Keywords: 
annotation; argumentative zoning; benchmark creation; scientific summarization
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Abstract: 
Argumentative Zoning (AZ) is a tool to obtain informative summaries of scientific articles. Using AZ assumes the definition of the main rhetorical structure in scientific articles, which are, then, used for the summary creation. The unavailability of large AZ annotated benchmark datasets is a bottleneck to training AZ-based summarization algorithms. In this work, we present an annotation platform for an AZ that defines four categories (zones), Claim, Method, Result and Conclusion, that are used to label sentences selected from scientific articles. The proposed tool can be used both for collecting benchmark datasets, and to help the researchers to create their own sub-corpora.
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Project title: 
Artificial Researcher in Science: Efficient Scientific Publication Mining: 2241716 (Wirtschaftsagentur Wien Ein Fonds der Stadt Wien) 
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Research Areas: 
Information Systems Engineering: 25%
Computer Science Foundations: 50%
Computational System Design: 25%
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Science Branch: 
1020 - Informatik: 90%
5020 - Wirtschaftswissenschaften: 10%
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Appears in Collections:
Conference Paper

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