QuOTeS: Query-Oriented Technical Summarization

Author:

Ramirez-Orta Juan,Xamena Eduardo,Maguitman Ana,Soto Axel J.,Zanoto Flavia P.,Milios Evangelos

Publisher

Springer Nature Switzerland

Reference22 articles.

1. Zhang, J., Zhao, Y., Saleh, M., Liu., P.J.: PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. In: Proceedings of the 37th International Conference on Machine Learning, ICML’20. JMLR.org (2020)

2. Papineni, K., Roukos, S., Ward, T., Zhu, W.-J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA (2002). Association for Computational Linguistics

3. Lin, C.-Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81, Barcelona, Spain (2004). Association for Computational Linguistics

4. Banerjee, S., Lavie, A.: METEOR: an automatic metric for MT evaluation with improved correlation with human judgments. In: Proceedings of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, pp. 65–72, Ann Arbor, Michigan (2005). Association for Computational Linguistics

5. Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 379–389, Lisbon, Portugal (2015). Association for Computational Linguistics

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