  Novel neural architectures, training strategies, and the availability of
large-scale corpora haven been the driving force behind recent progress in
abstractive text summarization. However, due to the black-box nature of neural
models, uninformative evaluation metrics, and scarce tooling for model and data
analysis, the true performance and failure modes of summarization models remain
largely unknown. To address this limitation, we introduce SummVis, an
open-source tool for visualizing abstractive summaries that enables
fine-grained analysis of the models, data, and evaluation metrics associated
with text summarization. Through its lexical and semantic visualizations, the
tools offers an easy entry point for in-depth model prediction exploration
across important dimensions such as factual consistency or abstractiveness. The
tool together with several pre-computed model outputs is available at
https://github.com/robustness-gym/summvis.
