@inproceedings{susilo-etal-2026-contextual,
    title = "Contextual Diversity Measure ({CDM}) for Controllable Story Generation in Large Language Models",
    author = "Susilo, Richard  and
      Suominen, Hanna  and
      Haslum, Patrik",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.98/",
    pages = "1125--1138",
    ISBN = "979-8-89176-393-7",
    abstract = "Scenario-based text generation has broad applications across education and creative writing, but remains underexplored in controllable text generation. We introduce the Contextual Diversity Measure (CDM), a metric that quantifies semantic diversity for scenario generation under fixed abstract semantic constraints, and validate it through controlled experiments. Statistical analysis across four embedding models demonstrates that CDM successfully distinguishes between high-diversity and low-diversity text pairs, with all tests achieving statistical significance at $p < 0.05$ on both the manually curated and LLM-generated subsets of the dataset. Effect sizes range from small-to-medium (Cohen{'}s $d$: 0.292{--}0.508) on the former and medium-to-large (Cohen{'}s $d$: 0.677{--}1.195) on the latter. Baseline comparisons indicate that CDM achieves excellent discrimination accuracy (100{\%} and 91.9{\%}, respectively), with discriminative power up to $5.5\times$ greater than the best baseline."
}