# Guided Exploration of Data Summaries

arXiv: https://arxiv.org/abs/2205.13956 · HTML: https://mlsanity.com/paper/2205.13956/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 53 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 4 of 4)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [Effortless Data Exploration with zenvisage: An Expressive and Interactive Visual Analytics System](https://mlsanity.com/paper/1604.03583/) (2016)
- [Interactive Summarization and Exploration of Top Aggregate Query Answers](https://mlsanity.com/paper/1807.11634/) (2018)

## Built on by (most cited first, 1 of 1)

- [Time-to-Pattern: Information-Theoretic Unsupervised Learning for Scalable Time Series Summarization](https://mlsanity.com/paper/2308.13722/) (2023)

## Abstract (arXiv)

Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that are collectively diverse to be representative. Uniformity addresses interpretability and diversity addresses representativity. Finding such as summary is a difficult task when data is highly diverse and large. We examine the applicability of Exploratory Data Analysis (EDA) to data summarization and formalize Eda4Sum, the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility. EdA4Sum generalizes one-shot summarization. We propose to solve it with one of two approaches: (i) Top1Sum which chooses the most useful summary at each step; (ii) RLSum which trains a policy with Deep Reinforcement Learning that rewards an agent for finding a diverse and new collection of uniform sets at each step. We compare these approaches with one-shot summarization and top-performing EDA solutions. We run extensive experiments on three large datasets. Our results demonstrate the superiority of our approaches for summarizing very large data, and the need to provide guidance to domain experts.