  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.
