# Web Table Extraction, Retrieval and Augmentation: A Survey

arXiv: https://arxiv.org/abs/2002.00207 · HTML: https://mlsanity.com/paper/2002.00207/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2020.

## Built on by (most cited first, 15 of 15)

- [TCN: Table Convolutional Network for Web Table Interpretation](https://mlsanity.com/paper/2102.09460/) (2021)
- [Pneuma: Leveraging LLMs for Tabular Data Representation and Retrieval in an End-to-End System](https://mlsanity.com/paper/2504.09207/) (2025)
- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [Towards Neural Schema Alignment for OpenStreetMap and Knowledge Graphs](https://mlsanity.com/paper/2107.13257/) (2021)
- [Beyond a Single Extractor: Re-thinking HTML-to-Text Extraction for LLM Pretraining](https://mlsanity.com/paper/2602.19548/) (2026)
- [Enhancing Tabular Reasoning with Pattern Exploiting Training](https://mlsanity.com/paper/2210.12259/) (2022)
- [Tab2KG: Semantic Table Interpretation with Lightweight Semantic Profiles](https://mlsanity.com/paper/2302.01150/) (2023)
- [Table Caption Generation in Scholarly Documents Leveraging Pre-trained Language Models](https://mlsanity.com/paper/2108.08111/) (2021)
- [Rows from Many Sources: Enriching row completions from Wikidata with a pre-trained Language Model](https://mlsanity.com/paper/2204.07014/) (2022)
- [TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes](https://mlsanity.com/paper/2407.01619/) (2024)
- [Type Prediction Systems](https://mlsanity.com/paper/2104.01207/) (2021)
- [Relation Extraction from Tables using Artificially Generated Metadata](https://mlsanity.com/paper/2108.10750/) (2021)
- [Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipedia](https://mlsanity.com/paper/2210.06353/) (2022)
- [Conceptual Schema Inference for Tabular Datasets using Large Language Models](https://mlsanity.com/paper/2509.04632/) (2025)
- [FollowTable: A Benchmark for Instruction-Following Table Retrieval](https://mlsanity.com/paper/2605.00400/) (2026)

## Abstract (arXiv)

Tables are a powerful and popular tool for organizing and manipulating data. A vast number of tables can be found on the Web, which represents a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.