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DocEng'2020 Competition on Extractive Text Summarization

Published: 29 September 2020 Publication History

Abstract

The DocEng'2020 Competition on Extractive Text Summarization assessed the performance of six new methods and fourteen classical algorithms for extractive text sumarization. The systems were evaluated using the CNN-Corpus, the largest test set available today for single document extractive summarization using two different strategies and the ROUGE and the direct match measures.

References

[1]
Rafael Dueire Lins, Rafael Ferreira Mello, and Steven Simske. 2019. DocEng'19 Competition on Extractive Text Summarization. In DocEng. ACM, 11--12.
[2]
Rafael Dueire Lins, Hilário Tomaz, Rafael Ferreira, Bruno Avila, Luciano Cabral, Jamilson Batista, Gabriel Silva, Rinaldo Lima, and Steven Simske. 2019. The CNN-Corpus: A Large Textual Corpus for Single-Document Extractive Summarization. In DocEng. ACM, 1--10.
[3]
Rafael Ferreira, Luciano de Souza Cabral, Rafael Dueire Lins, Gabriel Pereira e Silva, Fred Freitas, George DC Cavalcanti, Rinaldo Lima, Steven J Simske, and Luciano Favaro. 2013. Assessing sentence scoring techniques for extractive text summarization. Expert systems with applications 40, 14 (2013), 5755--5764.
[4]
Rodrigo Garcia, Rinaldo Lima, Bernard Espinasse, and Hilário Oliveira. 2017. Towards coherent single-document summarization: an integer linear programming-based approach. In In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC'18). IEEE, 712--719.
[5]
Daniel Lee, Rakesh Verma, Das Avisha, and Arjun Mukherjee. 2020. Experiments in Extractive Summarization: Integer Linear Programming, Term/Sentence Scoring, and Title-driven Models. arXiv preprint arXiv:2008.00140 21, 4 (2020), 787--798.
[6]
Chin-Yew Lin. 2004. ROUGE: A Package for Automatic Evaluation of Summaries. In ACL-04 Workshop, Marie-Francine Moens and Stan Szpakowicz (Eds.). Association for Computational Linguistics, Barcelona, Spain, 74--81.
[7]
Hilário Oliveira, Rafael Dueire Lins, Rinaldo Lima, Fred Freitas, and Steven J Simske. 2017. A Regression-Based Approach Using Integer Linear Programming for Single-Document Summarization. In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 270--277.
[8]
Hosein Rezaei, Seyed Amid Moeinzadeh, Azar Shahgholian, and Mohamad Saraee. 2019. Features in Extractive Supervised Single-document Summarization: Case of Persian News. arXiv:cs.CL/1909.02776
[9]
Tim Roughgarden. 2019. Beyond Worst-Case Analysis. Commun. ACM 62, 3 (2019), 88--96.
[10]
Rakesh Verma and Daniel Lee. 2017. Extractive Summarization: Limits, Compression, Generalized Model and Heuristics. Computación y Sistemas 21, 4 (2017), 787--798.

Cited By

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  • (2023)State-of-the-art approach to extractive text summarization: a comprehensive reviewMultimedia Tools and Applications10.1007/s11042-023-14613-982:19(29135-29197)Online publication date: 16-Feb-2023
  1. DocEng'2020 Competition on Extractive Text Summarization

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    Published In

    cover image ACM Conferences
    DocEng '20: Proceedings of the ACM Symposium on Document Engineering 2020
    September 2020
    130 pages
    ISBN:9781450380003
    DOI:10.1145/3395027
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 September 2020

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    Author Tags

    1. CNN Corpus
    2. NLP
    3. Text summarization
    4. text documents

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    • Refereed limited

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    DocEng '20
    Sponsor:
    DocEng '20: ACM Symposium on Document Engineering 2020
    September 29 - October 1, 2020
    CA, Virtual Event, USA

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    Overall Acceptance Rate 194 of 564 submissions, 34%

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    View all
    • (2023)State-of-the-art approach to extractive text summarization: a comprehensive reviewMultimedia Tools and Applications10.1007/s11042-023-14613-982:19(29135-29197)Online publication date: 16-Feb-2023

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