@inproceedings{vig-etal-2021-summvis,
title = "{S}umm{V}is: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization",
author = "Vig, Jesse and
Kryscinski, Wojciech and
Goel, Karan and
Rajani, Nazneen",
editor = "Ji, Heng and
Park, Jong C. and
Xia, Rui",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-demo.18",
doi = "10.18653/v1/2021.acl-demo.18",
pages = "150--158",
abstract = "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 \url{https://summvis.com}.",
}
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<abstract>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://summvis.com.</abstract>
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%0 Conference Proceedings
%T SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization
%A Vig, Jesse
%A Kryscinski, Wojciech
%A Goel, Karan
%A Rajani, Nazneen
%Y Ji, Heng
%Y Park, Jong C.
%Y Xia, Rui
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F vig-etal-2021-summvis
%X 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://summvis.com.
%R 10.18653/v1/2021.acl-demo.18
%U https://aclanthology.org/2021.acl-demo.18
%U https://doi.org/10.18653/v1/2021.acl-demo.18
%P 150-158
Markdown (Informal)
[SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization](https://aclanthology.org/2021.acl-demo.18) (Vig et al., ACL-IJCNLP 2021)
ACL