@inproceedings{chatterjee-etal-2026-perducer,
    title = "{P}er{D}ucer: Keyphrase-Driven Personalization Inducer for Summarization from User Histories",
    author = "Chatterjee, Parthiv  and
      Batha, Asish Joel  and
      Dasgupta, Sourish  and
      Chakraborty, Tanmoy",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings 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.findings-acl.1035/",
    pages = "20651--20677",
    ISBN = "979-8-89176-395-1",
    abstract = "Document summarization becomes more challenging when summaries must reflect a user{'}s subjective interests in addition to document salience. SOTA Large Language Models (LLMs) show strong in-context summarization capabilities. Prior works report that simply prepending long and dynamically evolving user histories leads to unstable, inconsistent personalization. To address this, we introduce PerDucer, a personalization inducer for frozen language models. Given a user interaction sequence (trajectory) and a query document, PerDucer first predicts the next likely preference signal. It then maps the latent signal to a small set of personalized keyphrases for the query document. These keyphrases serve as the control cues that steer the frozen summarizers (both LLMs and SLMs) towards user-aligned summaries. Across the PENS and OpenAI-Reddit benchmarks, PerDucer-boosted LLMs consistently outperform the strongest history-prompting baselines, yielding an average +0.18 improvement across personalization metrics (PerSEval in our case). Two PerDucer-augmented SLMs approach the top-performing evaluated LLM, with SmolLM2-1.7B reaching 97{\%} of the best-performing DeepSeek-R1-14B score. These results indicate that short keyphrase cues can induce personalization in frozen summarizers without modifying their parameters."
}