@inproceedings{xiong-etal-2026-progressive,
    title = "Progressive Visual Refinement for Multi-modal Summarization",
    author = "Xiong, Ye  and
      Kamigaito, Hidetaka  and
      Murakami, Soichiro  and
      Zhang, Peinan  and
      Takamura, Hiroya  and
      Okumura, Manabu",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-short.5/",
    pages = "93--104",
    ISBN = "979-8-89176-381-4",
    abstract = "Multi-modal summarization (MMS) has emerged as a critical research area driven by the proliferation of multimedia content, focusing on generating condensed summaries by cross-modal complementary information synthesis. Previous studies have demonstrated the effectiveness of heterogeneous fusion paradigms, particularly through visual-centric feature extraction mechanisms, in constructing cross-modal representations that yield substantial performance gains. Nevertheless, the comprehensive utilization of multimodal information along with the intricate interdependencies among textual content, visual elements, and the summary generation process has been still insufficiently explored. We propose the Patch-Refined Visual Information Network (PRVIN) to address the insufficient exploitation of visual information. The essential patch selector and patch refiner components in PRVIN work collaboratively to progressively identify and refine critical visual features. An additional vision-to-summary alignment mechanism is also introduced to enhance the semantic connections between multi-modal representations and summary outputs. Extensive experiments conducted on two public MMS benchmark datasets demonstrate the superiority of PRVIN while quantitatively validating the crucial role of comprehensive visual information utilization in MMS tasks."
}