Introduction
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-07214-7_1
Reference47 articles.
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2. Bowman, S.R., Dahl, G.: What will it take to fix benchmarking in natural language understanding? In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4843–4855. Association for Computational Linguistics, Stroudsburg, PA, USA (2021). https://doi.org/10.18653/v1/2021.naacl-main.385
3. Cabrera-Diego, L.A., et al.: Evaluating multiple summaries without human models: a first experiment with a trivergent model. Lect. Notes Comput. Sci. 7934(1), 91–101 (2016). https://doi.org/10.1016/S1003-6326(11)60841-8
4. Cabrera-Diego, L.A., Torres-Moreno, J.-M.: SummTriver: a new trivergent model to evaluate summaries automatically without human references. Data Knowl. Eng. 113, 184–197 (2018). https://doi.org/10.1016/j.datak.2017.09.001
5. Conroy, J.M., Dang, H.T.: Mind the gap: dangers of divorcing evaluations of summary content from linguistic quality. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 145–152. Association for Computational Linguistics, Manchester (2008)
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