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DocEng'19 Competition on Extractive Text Summarization

Published: 23 September 2019 Publication History

Abstract

The DocEng'19 Competition on Extractive Text Summarization assessed the performance of two new and fourteen previously published extractive text sumarization methods. The competitors were evaluated using the CNN-Corpus, the largest test set available today for single document extractive summarization.

References

[1]
Rafael Dueire Lins, Hilário Tomaz, Rafael Ferreira, Bruno Avila, Luciano Cabral, Jamilson Batista, Gabriel Silva, and Steven Lima, Rinaldoand Simske. 2019. The CNN-Corpus: A Large Textual Corpus for Single-Document Extractive Summarization. In DocEng. ACM, 1--10.
[2]
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.
[3]
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.
[4]
Ramesh Nallapati, Feifei Zhai, and Bowen Zhou. 2017. Summarunner: A recurrent neural network based sequence model for extractive summarization of documents. In AAAI Conference on Artificial Intelligence.
[5]
Hilário Oliveira, Rinaldo Lima, Rafael Dueire Lins, Fred Freitas, Marcelo Riss, and Steven J Simske. 2016. A concept-based integer linear programming approach for single-document summarization. In BRACIS. IEEE, 403--408.

Cited By

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  • (2023)EXABSUM: a new text summarization approach for generating extractive and abstractive summariesJournal of Big Data10.1186/s40537-023-00836-y10:1Online publication date: 24-Oct-2023
  • (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
  • (2020)Automatic Text Summarization: A Comprehensive SurveyExpert Systems with Applications10.1016/j.eswa.2020.113679(113679)Online publication date: Jul-2020
  • Show More Cited By
  1. DocEng'19 Competition on Extractive Text Summarization

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      Published In

      cover image ACM Conferences
      DocEng '19: Proceedings of the ACM Symposium on Document Engineering 2019
      September 2019
      254 pages
      ISBN:9781450368872
      DOI:10.1145/3342558
      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: 23 September 2019

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      Author Tags

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

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      • Abstract
      • Research
      • Refereed limited

      Conference

      DocEng '19
      Sponsor:
      DocEng '19: ACM Symposium on Document Engineering 2019
      September 23 - 26, 2019
      Berlin, Germany

      Acceptance Rates

      DocEng '19 Paper Acceptance Rate 30 of 77 submissions, 39%;
      Overall Acceptance Rate 194 of 564 submissions, 34%

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      Cited By

      View all
      • (2023)EXABSUM: a new text summarization approach for generating extractive and abstractive summariesJournal of Big Data10.1186/s40537-023-00836-y10:1Online publication date: 24-Oct-2023
      • (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
      • (2020)Automatic Text Summarization: A Comprehensive SurveyExpert Systems with Applications10.1016/j.eswa.2020.113679(113679)Online publication date: Jul-2020
      • (2019)The CNN-CorpusProceedings of the ACM Symposium on Document Engineering 201910.1145/3342558.3345388(1-10)Online publication date: 23-Sep-2019

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