Nouveau-ROUGE: A Novelty Metric for Update Summarization
Open accessAbstract
An update summary should provide a fluent summarization of new information on a time-evolving topic, assuming that the reader has already reviewed older documents or summaries. In 2007 and 2008, an annual summarization evaluation included an update summarization task. Several participating systems produced update summaries indistinguishable from human-generated summaries when measured using ROUGE. However, no machine system performed near human-level performance in manual evaluations such as pyramid and overall responsiveness scoring. We present a metric called Nouveau-ROUGE that improves correlation with manual evaluation metrics and can be used to predict both the pyramid score and overall responsiveness for update summaries. Nouveau-ROUGE can serve as a less expensive surrogate for manual evaluations when comparing existing systems and when developing new ones.
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“While our multi-target setting allows us to mitigate some of the limitations of Rouge (Conroy et al., 2011; Cohan and Goharian, 2016), we acknowledge that relying only on automated metrics is insufficient for evaluating the quality of the models.”
From TLDR: Extreme Summarization of Scientific Documents (2020) · cites this paper as Conroy et al., 2011
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Research works printed but not linked: 9
- Conroy, John M. and Hoa Trang Dang. 2008. Mind the gap: Dangers of divorcing evaluations of summary content from linguistic quality. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 145-152, Manchester.
- Conroy, John M., Judith D. Schlesinger, and Dianne P. O'Leary. 2006. Topic-focused multi-document summarization using an approximate oracle score. In Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 152-159, Sydney.
- Conroy, John M., Judith D. Schlesinger, and Dianne P. O'Leary. 2007. CLASSY 2007 at DUC 2007. In Proceedings of the Seventh Document Understanding Conference (DUC), Rochester, NY. Available at http://duc. nist.gov/pubs.html#2007.
- Dang, Hoa Trang. 2007. Overview of DUC 2007. In Proceedings of the Seventh Document Understanding Conference (DUC), Rochester, NY. Available at http://duc.nist.gov/ pubs.html#2005.
- Hovy, Eduard, Chin-Yew Lin, and Liang Zhou. 2005. Evaluating DUC 2005 using basic elements. In Proceedings of the Fifth Document Understanding Conference (DUC), Vancouver. Available at www-nlpir/ nist.gov/projects/duc/pubs.html.
- Lin, Chin-Yew and Eduard Hovy. 2000. The automated acquisition of topic signatures for text summarization. In Proceedings of the 18th Conference on Computational Linguistics, pages 495-501, Morristown, NJ.
- Mani, Inderjeet, Therese Firmin, David House, Gary Klein, Beth Sundheim, and Lynette Hirschman. 1999. The TIPSTER SUMMAC text summarization evaluation. In Proceedings of EACL'99: Ninth Conference of the European Chapter of the Association for Computational Linguistics, pages 77-85, Bergen.
- Nenkova, Ani and Rebecca Passonneau. 2004. Evaluating content selection in summarization: The pyramid method. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pages 145-152, Boston, MA.
- Schlesinger, Judith D., Dianne P. O'Leary, and John M. Conroy. 2008. Arabic/ English multi-document summarization with CLASSY-The past and the future. In Alexander F. Gelbukh, editor, CICLing, volume 4919 of Lecture Notes in Computer Science. Springer, Haifa, pages 568-581.
Cited by
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TLDR: Extreme Summarization of Scientific Documents
Cachola, I., Lo, K., Cohan, A., & Weld, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.428 · Cited by 169 · see citing works
“Human evaluation While our multi-target setting allows us to mitigate some of the limitations of Rouge (Conroy et al., 2011; Cohan and Goharian, 2016), we acknowledge that relying only on automated metrics is insufficient for evaluating the quality of the models.”
Method
- Authors
- Conroy, J. M., Schlesinger, J. D., & O'Leary, D. P.
- Affiliations
- IDA/Center for Computing Sciences
- University of Maryland
- ORCID
- Conroy, John M.
- Venue
- Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2011-03
- Volume
- 37
- Issue
- 1
- Pages
- 1-8
- ISSN
- 0891-2017 (ISSN-L)
- 1530-9312
- OpenAlex
- W1983018376
- Cited by
- 26
- References
- 0
- Citations by year
- 2023: 1 · 2022: 1 · 2021: 2 · 2020: 1 · 2019: 1 · 2018: 3 · 2017: 6 · 2016: 1 · 2014: 2 · 2013: 4 · 2012: 2
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computational Linguistics (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- update summarization, ROUGE, responsiveness, summarization evaluation
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