@inproceedings{orwig-etal-2025-mondrian,
    title = "Mondrian: A Framework for Logical Abstract (Re)Structuring",
    author = "Orwig, Elizabeth Grace  and
      Park, Shinwoo  and
      Jin, Hyundong  and
      Han, Yo-Sub",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1709/",
    pages = "33663--33678",
    ISBN = "979-8-89176-332-6",
    abstract = "The well-known rhetorical framework, ABT (And, But, Therefore), mirrors natural human cognition in structuring an argument{'}s logical progression - apropos to academic communication. However, distilling the complexities of research into clear and concise prose requires careful sequencing of ideas and formulating clear connections between them. This presents a quiet inequitability for contributions from authors who struggle with English proficiency or academic writing conventions. We see this as impetus to introduce: Mondrian, a framework that identifies the key components of an abstract and reorients itself to properly reflect the ABT logical progression. The framework is composed of a deconstruction stage, reconstruction stage, and rephrasing. We introduce a novel metric for evaluating deviation from ABT structure, named EB-DTW, which accounts for both ordinality and a non-uniform distribution of importance in a sequence. Our overall approach aims to improve the comprehensibility of academic writing, particularly for non-native English speakers, along with a complementary metric. The effectiveness of Mondrian is tested with automatic metrics and extensive human evaluation, and demonstrated through impressive quantitative and qualitative results, with organization and overall coherence of an abstract improving by an average of 27.71{\%} and 24.71{\%}."
}