SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
This tool addresses the lack of interpretability and analysis capabilities in text summarization for researchers and practitioners, but it is incremental as it builds on existing visualization and evaluation methods.
The authors tackled the problem of understanding the performance and failure modes of abstractive text summarization models by introducing SummVis, an open-source tool for visualizing models, data, and evaluation metrics, enabling fine-grained analysis across dimensions like factual consistency.
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.