Open Access
Issue
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
Article Number 02008
Number of page(s) 6
Section Machine Learning, Deep Learning, and Applications
DOI https://doi.org/10.1051/itmconf/20257302008
Published online 17 February 2025
  1. R. Mihalcea, P. Tarau, TextRank: Bringing order into texts, in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP), (2004), 404-411 [Google Scholar]
  2. A. Nenkova, K. McKeown, Automatic summarization. Found. Trends Inf. Retr. 5, 103-233 (2011) [CrossRef] [Google Scholar]
  3. A. Nenkova, R. Passonneau, K. McKeown, Evaluating content contribution in multidocument summarization, in Proceedings of the Human Language Technology Conference of the NAACL, Main Conference, (2006), 100-107 [Google Scholar]
  4. E. Barrios, et al., Multi-document summarization via information extraction and reorganization, in Proceedings of the 27th International Conference on Computational Linguistics (COLING), (2018), 302-312 [Google Scholar]
  5. Y. Zhong, H. Poon, Extractive summarization with deep learning, in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), (2019), 1637-1646 [Google Scholar]
  6. J. Doe, J. Smith, Fine-tune BERT for Extractive Summarization. J. Nat. Lang. Process. 15, 123-145 (2023) [Google Scholar]
  7. R. Nogueira, K. Cho, MS MARCO Passage Ranking: A Deep Learning Perspective. J. Inf. Retr. 12, 385-402 (2019) [Google Scholar]
  8. J. Zhang, Y. Zhao, R. Salakhutdinov, Fine-tune BERT for Extractive Summarization. arXiv preprint arXiv (2019) [Google Scholar]
  9. J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2019) [Google Scholar]
  10. J. Zhang, Y. Zhao, M. Saleh, et al., Pegasus: Pre-training with extracted gap-sentences for abstractive summarization, in Proceedings of the International Conference on Machine Learning, PMLR, (2020), 11328-11339 [Google Scholar]
  11. C. Raffel, J. Schmidt, T. BART, T5: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.