@inproceedings{ryu-etal-2026-exploring,
    title = "Exploring Iterative Controllable Summarization with Large Language Models",
    author = "Ryu, Sangwon  and
      Do, Heejin  and
      Kim, Daehui  and
      Yu, Hwanjo  and
      Kim, Dongwoo  and
      Kim, Yunsu  and
      Lee, Gary  and
      Ok, Jungseul",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.26/",
    pages = "512--528",
    ISBN = "979-8-89176-386-9",
    abstract = "Large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., length or topic) remains underexplored, limiting their adaptability to specific user preferences. In this paper, we systematically explore the controllability of LLMs. To this end, we revisit summary attribute measurements and introduce iterative evaluation metrics, failure rate and average iteration count, to more precisely evaluate controllability beyond assessment of errors. Our findings show that LLMs struggle more with numerical attributes than with linguistic attributes. To address this challenge, we propose a guide-to-explain framework (GTE) for controllable summarization. GTE enables the model to identify misaligned attributes in the initial draft and guides it to self-explain errors in the previous output. By encouraging reflection on attribute misalignment, GTE generates well-adjusted summaries that satisfy the desired attributes with robust effectiveness while requiring surprisingly fewer iterations than other iterative approaches."
}