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Text summarization is a downstream natural language processing (NLP) task that challenges the understanding and generation capabilities of language models.
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Lin, C. Y. (2004, July). ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out (pp. 74-81)
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Pilault, J., Li, R., Subramanian, S., & Pal, C. (2020, November). On extractive and abstractive neural document summarization with transformer language models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 9308-9319)
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Stiennon, N., Ouyang, L., Wu, J., Ziegler, D., Lowe, R., Voss, C., … & Christiano, P. F. (2020). Learning to summarize with human feedback. Advances in Neural Information Processing Systems, 33, 3008-3021
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El-Kassas, W. S., Salama, C. R., Rafea, A. A., & Mohamed, H. K. (2021). Automatic text summarization: A comprehensive survey. Expert Systems with Applications, 165, 113679
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Fabbri, A. R., Kryściński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021). SummEval: Re-evaluating summarization evaluation. Transactions of the Association for Computational Linguistics, 9, 391-409
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Zhang, Y., Ni, A., Mao, Z., Wu, C. H., Zhu, C., Deb, B., … & Zhang, R. (2021). Summ N
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Vasilyev, O., & Bohannon, J. (2021, November). ESTIME: Estimation of summary-to-text inconsistency by mismatched embeddings. In Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems (pp. 94-103)
2021
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Koh, H. Y., Ju, J., Liu, M., & Pan, S. (2022). An empirical survey on long document summarization: datasets, models, and metrics. ACM Computing Surveys, 55(8), 1-35
2022
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Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., … & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744
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Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., … & Fung, P. (2023). Survey of hallucination in natural language generation. ACM Computing Surveys, 55(12), 1-38
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