  Generating useful network summaries is a challenging and important problem
with several applications like sensemaking, visualization, and compression.
However, most of the current work in this space do not take human feedback into
account while generating summaries. Consider an intelligence analysis scenario,
where the analyst is exploring a similarity network between documents. The
analyst can express her agreement/disagreement with the visualization of the
network summary via iterative feedback, e.g. closing or moving documents
("nodes") together. How can we use this feedback to improve the network summary
quality? In this paper, we present NetReAct, a novel interactive network
summarization algorithm which supports the visualization of networks induced by
text corpora to perform sensemaking. NetReAct incorporates human feedback with
reinforcement learning to summarize and visualize document networks. Using
scenarios from two datasets, we show how NetReAct is successful in generating
high-quality summaries and visualizations that reveal hidden patterns better
than other non-trivial baselines.
