@inproceedings{do-etal-2026-conras,
    title = "{C}on{RAS}: Contrastive In-context Learning Framework for Retrieval-Augmented Summarization",
    author = "Do, Juseon  and
      Han, Sungwoo  and
      Kwon, Jingun  and
      Kamigaito, Hidetaka  and
      Okumura, Manabu",
    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.255/",
    pages = "4885--4900",
    ISBN = "979-8-89176-386-9",
    abstract = "Contrastive learning (CL) has achieved remarkable progress in natural language processing (NLP), primarily as a paradigm for pre-training and fine-tuning. However, its potential during the generation phase, particularly in in-context learning (ICL)-based retrieval-augmented summarization, remains largely unexplored. While previous studies have attempted to incorporate negative samples into ICL prompts, these methods do not enforce a true contrastive objective that encourages separation of positive and negative samples in the representation space. In this paper, we first demonstrate through preliminary experiments that small language models (SLMs) can interpret contrastive prompts and effectively distinguish between positive and negative samples during inference, without any parameter updates. Building on these findings, we propose ConRAS, a novel framework that injects contrastive objectives into ICL-based retrieval-augmented summarization. Extensive experiments and in-depth analysis on three summarization benchmarks using four SLMs show that ConRAS consistently outperforms state-of-the-art retrieval-augmented methods, achieving significant improvements in summary quality."
}