# Cartolabe: A Web-Based Scalable Visualization of Large Document Collections

arXiv: https://arxiv.org/abs/2003.00975 · HTML: https://mlsanity.com/paper/2003.00975/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization](https://mlsanity.com/paper/2307.11770/) (2023)
- [A Hybrid Intelligence Method for Argument Mining](https://mlsanity.com/paper/2403.09713/) (2024)
- [HINTs: Sensemaking on large collections of documents with Hypergraph visualization and INTelligent agents](https://mlsanity.com/paper/2403.02752/) (2024)
- [An Interdisciplinary Perspective on Evaluation and Experimental Design for Visual Text Analytics: Position Paper](https://mlsanity.com/paper/2209.11534/) (2022)

## Abstract (arXiv)

We describe CARTOLABE, a web-based multi-scale system for visualizing and exploring large textual corpora based on topics, introducing a novel mechanism for the progressive visualization of filtering queries. Initially designed to represent and navigate through scientific publications in different disciplines, CARTOLABE has evolved to become a generic framework and accommodate various corpora, ranging from Wikipedia (4.5M entries) to the French National Debate (4.3M entries). CARTOLABE is made of two modules: the first relies on Natural Language Processing methods, converting a corpus and its entities (documents, authors, concepts) into high-dimensional vectors, computing their projection on the 2D plane, and extracting meaningful labels for regions of the plane. The second module is a web-based visualization, displaying tiles computed from the multidimensional projection of the corpus using the U MAP projection method. This visualization module aims at enabling users with no expertise in visualization and data analysis to get an overview of their corpus, and to interact with it: exploring, querying, filtering, panning and zooming on regions of semantic interest. Three use cases are discussed to illustrate CARTOLABE's versatility and ability to bring large scale textual corpus visualization and exploration to a wide audience.