@inproceedings{spangher-etal-2022-sequentially,
    title = "Sequentially Controlled Text Generation",
    author = "Spangher, Alexander  and
      Ming, Yao  and
      Hua, Xinyu  and
      Peng, Nanyun",
    editor = "Goldberg, Yoav  and
      Kozareva, Zornitsa  and
      Zhang, Yue",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.findings-emnlp.509/",
    doi = "10.18653/v1/2022.findings-emnlp.509",
    pages = "6848--6866",
    abstract = "While GPT-2 generates sentences that are remarkably human-like, longer documents can ramble and do not follow human-like writing structure. We study the problem of imposing structure on long-range text. We propose a novel controlled text generation task, sequentially controlled text generation, and identify a dataset, NewsDiscourse as a starting point for this task. We develop a sequential controlled text generation pipeline with generation and editing. We test different degrees of structural awareness and show that, in general, more structural awareness results in higher control- accuracy, grammaticality, coherency and topicality, approaching human-level writing performance."
}