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Progressive Visual Refinement for Multi-modal Summarization

Paper · 2026 · Ye Xiong, Hidetaka Kamigaito, Soichiro Murakami et al.

Summary

Multi-modal summarization (MMS) produces a short text summary from inputs such as an article and its images. Earlier fusion models tend to underuse useful visual content, let irrelevant image regions in, and lean too heavily on the text. The authors propose PRVIN (Patch-Refined Visual Information Network), which treats individual ViT image patches as the units to keep or discard. An essential patch selector sits inside the ViT. It scores each patch's relevance to the text and the summary and keeps the top-ranked patches. A transformer-decoder patch refiner then takes a smaller subset and enhances it by cross-attending to the larger kept set. The refined patches are concatenated with BART-base text encodings and decoded into a summary. Training also includes a vision-only task that generates the summary from images alone, which strengthens the image–summary link. The selector is supervised with oracle labels built from image–text similarity, checked by an object detector. PRVIN roughly matches the strongest prior model, VE-ELIN, on MMSS and beats all baselines on MM-Sum-En. This suggests that careful patch-level filtering makes images more useful for summarization.

Key points

  • Benchmarks:
    • MMSS: 62,000/2,000/2,000 sentence–image–summary triplets.
    • MM-Sum-En: English portion of a BBC News multilingual MMS corpus, with 303,828/11,437/11,460 train/validation/test samples.
  • Settings: the selector keeps 70% of patches (top-k) and the refiner keeps 60% (top-j). The three losses (generation, patch-selection BCE, vision-to-summary) are weighted with α = β = 1.0.
  • MMSS results: PRVIN is close to VE-ELIN on ROUGE-1/2/L (54.16/31.19/51.31 vs. 54.20/31.24/51.47). It is slightly ahead on BLEU (60.26) and MoverScore (49.32).
  • MM-Sum-En results: PRVIN tops all baselines, including VE-ELIN (R-1 39.97) and SOV-MAS (R-1 37.26). It reaches:
    • ROUGE-1/2/L: 40.26/18.37/32.61
    • BLEU: 45.90
    • BERTScore: 97.04
  • Ablations:
    • Removing both selector and refiner lowers R-1 to 51.03 on MMSS and 38.12 on MM-Sum-En.
    • Removing the refiner alone or the vision-summary loss also hurts performance.
    • Masking either the text or the visual part of the fused representation causes clear drops, indicating that both modalities are used.
    • Stated limitations: tests cover only English and the text+image setting, and results depend on oracle quality.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Nara Institute of Science and TechnologyIkoma · Japan48 works · 4,565 citations🎓Institute of Science TokyoTokyo · Japan14 works · 20 citations🏢CyberAgentTokyo · Japan2 works · 3 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

🧭 Topics 2Research subjects and languages the work is about.Vision & language · 811 worksSummarization · 243 works

Cite

Cite as Ye Xiong, Hidetaka Kamigaito, Soichiro Murakami, Peinan Zhang, Hiroya Takamura and Manabu Okumura. 2026. Progressive Visual Refinement for Multi-modal Summarization. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-short.5

@inproceedings{xiong2026progressive,
  title = {{Progressive Visual Refinement for Multi-modal Summarization}},
  author = {Ye Xiong, Hidetaka Kamigaito, Soichiro Murakami, Peinan Zhang, Hiroya Takamura and Manabu Okumura},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-short.5},
  url = {https://doi.org/10.18653/v1/2026.eacl-short.5}
}

References

3 references, 3 of them with a card of ours. ↗ leads to the work.

  1. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. 2025. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923. — Qwen2.5-VL Technical Report
  2. Iacer Calixto, Qun Liu, and Nick Campbell. 2017. Doubly-attentive decoder for multi-modal neural machine translation. arXiv preprint arXiv:1702.01287.
  3. Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. 2020. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104–120. Springer. \#\# Conclusion Jacob Devlin. 2018. Bert: Pre-training of deep bidire…

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