Visual Segmentation for Information Extraction from Heterogeneous Visually Rich Documents
Summary: VS2 segments visually rich documents into logical blocks via document-type-agnostic cues. A distantly supervised search-and-select uses block boundaries to locate entities, outperforming text-only IE across three heterogeneous datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Ritesh Sarkhel (Ohio State University)
- 2. Arnab Nandi (Ohio State University)
BibTeX Citation
@inproceedings{sarkhel_sigmod19,
title = {{Visual Segmentation for Information Extraction from Heterogeneous Visually Rich Documents}},
author = {Sarkhel, Ritesh and Nandi, Arnab},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3319867},
url = {https://dl.acm.org/doi/10.1145/3299869.3319867},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,398 | Improving Information Extraction from Visually Rich Documents using Visual Span Representations | 2021 | VLDB | 5.2755515e-05 |
| 9,399 | Glean: Structured Extractions from Templatic Documents | 2021 | VLDB | 5.2755515e-05 |
| 11,455 | Self-Training for Label-Efficient Information Extraction from Semi-Structured Web-Pages | 2023 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 503 | RoadRunner: Towards Automatic Data Extraction from Large Web Sites | 2001 | VLDB | 0.00017314037 |
| 3,192 | Fonduer: Knowledge Base Construction from Richly Formatted Data | 2018 | SIGMOD | 7.65035e-05 |
| 4,054 | Enterprise Information Extraction: Recent Developments and Open Challenges | 2010 | SIGMOD | 6.9352304e-05 |
| 6,308 | Extracting Logical Hierarchical Structure of HTML Documents Based on Headings | 2015 | VLDB | 5.9193546e-05 |
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