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Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences.

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Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data., , , , , , , , , and 2 other author(s). COLING (Industry), page 64-77. International Committee on Computational Linguistics, (2020)Acquire, adapt, and anticipate: continuous learning to block malicious domains., , , and . IEEE BigData, page 1891-1898. IEEE, (2018)Getting to Production with Few-shot Natural Language Generation Models., , , , , , , , , and 2 other author(s). SIGDIAL, page 66-76. Association for Computational Linguistics, (2021)Learning Representations for Log Data in Cybersecurity., , , , , and . CSCML, volume 10332 of Lecture Notes in Computer Science, page 250-268. Springer, (2017)Shooting the moving target: machine learning in cybersecurity., and . OpML, page 13-14. USENIX Association, (2019)Improving Faithfulness of Abstractive Summarization by Controlling Confounding Effect of Irrelevant Sentences., , , , , , , and . CoRR, (2022)Building Adaptive Acceptability Classifiers for Neural NLG., , , , , , , , , and 2 other author(s). EMNLP (1), page 682-697. Association for Computational Linguistics, (2021)Self Healing Capabilities in Polymeric Materials An Introduction to Techniques. International Journal of Trend in Scientific Research and Development, 3 (4): 735-738 (May 2019)