DBScholar

Back to papers

Table Extraction and Understanding for Scientific and Enterprise Applications

Summary: Survey and tutorial on table extraction and understanding for scientific and enterprise docs. From border/cell segmentation to semantic linking with headers, units, captions, and surrounding text, it surveys methods, open problems, and applications. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12410
Venue
VLDB
Year
2020
Pagerank
5.6078973e-05
Overall Rank
7,482 | 48.67%
DOI
10.14778/3415478.3415563

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{burdick_vldb20,
        title = {{Table Extraction and Understanding for Scientific and Enterprise Applications}},
        author = {Burdick, Douglas and Danilevsky, Marina and Evfimievski, Alexandre V and Katsis, Yannis and Wang, Nancy},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {3433--3436},
        doi = {10.14778/3415478.3415563},
        url = {https://doi.org/10.14778/3415478.3415563},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,403 ST-Raptor: LLM-Powered Semi-Structured Table Question Answering 2026 SIGMOD 5.093636e-05
10,405 AixelAsk: A Stepwise-Guided Retrieval and Reasoning Framework for Large Table QA 2026 SIGMOD 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers