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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
12222
Venue
VLDB
Year
2020
Pagerank
4.7339251e-05
Overall Rank
7,424 | 48.36%
DOI
10.14778/3415478.3415563

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,115 ST-Raptor: LLM-Powered Semi-Structured Table Question Answering 2026 SIGMOD 4.1945683e-05
10,117 AixelAsk: A Stepwise-Guided Retrieval and Reasoning Framework for Large Table QA 2026 SIGMOD 4.1945683e-05
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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.

Rank Cited Paper Year Venue Pagerank
107 WebTables: Exploring the Power of Tables on the Web 2008 VLDB 0.00048377684
1,001 Recovering Semantics of Tables on the Web 2011 VLDB 0.00014706505
3,155 Ten Years of WebTables 2018 VLDB 7.4672742e-05
3,285 Using the Structure of Web Sites for Automatic Segmentation of Tables 2004 SIGMOD 7.2759001e-05
3,303 Fonduer: Knowledge Base Construction from Richly Formatted Data 2018 SIGMOD 7.2487486e-05
8,467 Creation and Interaction with Large-scale Domain-Specific Knowledge Bases 2017 VLDB 4.504802e-05
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