Natural Language Generation (NLG) has received much attention with rapidly developing models and ever-more available data. As a result, a growing amount of work attempts to personalize these systems for better human interaction experience. Still, diverse sets of research across multiple dimensions and numerous levels of depth exist and are scattered across various communities. In this work, we survey the ongoing research efforts and introduce a categorization of these under the umbrella user-centric natural language generation. We further discuss some of the challenges and opportunities in NLG personalization.
CITATION STYLE
Yang, D., & Flek, L. (2021). Towards User-Centric Text-to-Text Generation: A Survey. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12848 LNAI, pp. 3–22). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-83527-9_1
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