@inproceedings{cachola-etal-2026-fine,
    title = "Fine-grained Readability Controlled Summarization of Scientific Documents via Control Vectors",
    author = "Cachola, Isabel  and
      Sasse, Kuleen  and
      Dredze, Mark",
    editor = "Mysore, Sheshera  and
      Kumar, Sachin  and
      Balachandran, Vidhisha  and
      Hayati, Shirley Anugrah  and
      Brahman, Faeze  and
      Moussa, Hanane Nour  and
      Salemi, Alireza",
    booktitle = "Proceedings of the Second Workshop on Customizable {NLP}: Progress and Challenges in Customizing {NLP} for a Domain, Application, Group, or Individual ({C}ustom{NLP}4{U})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.customnlp4u-1.10/",
    pages = "97--116",
    ISBN = "979-8-89176-396-8",
    abstract = "Plain Language Summarization (PLS) generates summaries of technical documents accessible to non-expert audiences. Readability {--} commonly used to evaluate PLS {--} has often been treated coarsely (expert vs. lay) although it exists on a spectrum with different levels for different readers. We propose a light weight control vector method for fine-grained readability control in scientific summarization along with a requirements-based framework for data selection. Our framework enforces: (1) readability levels differ substantially, and (2) paired examples share comparable content. Under this, control vectors enable more precise readability control than other popular methods."
}