Papers › SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

15 Apr 2021ACL 2021 5arXiv:2104.07605archive 2025-07-28

Jesse Vig, Wojciech Kryściński, Karan Goel, Nazneen Fatema Rajani

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.

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clean_text robustness-gym/summvis/utils.py official repository unverified Apache-2.0 (permissive) · 7c9b5b54376420a1 · report
postprocess_data robustness-gym/summvis/generation.py official repository unverified Apache-2.0 (permissive) · d5151d379c371c77 · report

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Abstractive Text SummarizationText Summarization

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