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Improving Information Extraction from Visually Rich Documents using Visual Span Representations

Summary: Artemis, a visually aware IE method for heterogeneous visually rich documents, encodes visual+textual+layout context into fixed-length span representations. Minimal supervision for visual-span boundaries; multimodal embeddings boost IE, up to 17 F1 points on four datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h9b27c07b821f84d5
Venue
VLDB
Year
2021
Pagerank
5.3835163e-05
Overall Rank
8,176 | 45.03%
DOI
10.14778/3446095.3446104

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{sarkhel_vldb21,
        title = {{Improving Information Extraction from Visually Rich Documents using Visual Span Representations}},
        author = {Sarkhel, Ritesh and Nandi, Arnab},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {5},
        pages = {822--834},
        doi = {10.14778/3446095.3446104},
        url = {https://doi.org/10.14778/3446095.3446104},
        year = {2021}
}

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