The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Problems in Current Text Simplification Research: New Data Can Help
Open accessAbstract
Simple Wikipedia has dominated simplification research in the past 5 years. In this opinion paper, we argue that focusing on Wikipedia limits simplification research. We back up our arguments with corpus analysis and by highlighting statements that other researchers have made in the simplification literature. We introduce a new simplification dataset that is a significant improvement over Simple Wikipedia, and present a novel quantitative-comparative approach to study the quality of simplification data resources.
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Cites
Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008) · Political Analysis · doi:10.1093/pan/mpn018 · Cited by 542 · cited 4×
“We choose the Monroe et al. (2008) method because many function words and punctuations are very frequent and play important roles in text simplification.”
MethodInfluentialRevisiting readability: a unified framework for predicting text quality
Pitler, E., & Nenkova, A. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613742 · Cited by 375
Cited by
Optimizing Statistical Machine Translation for Text Simplification
Xu, W., Napoles, C., Pavlick, E., Chen, Q., & Callison-Burch, C. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00107 · Cited by 521
Self-citationSentence Simplification with Deep Reinforcement Learning
Zhang, X., & Lapata, M. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1062 · Cited by 331
Pavlick, E., Rastogi, P., Ganitkevitch, J., Van Durme, B., & Callison-Burch, C. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) · doi:10.3115/v1/p15-2070 · Cited by 256
Self-citationParaphrasing evades detectors of AI-generated text, but retrieval is an effective defense
Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1195 · Cited by 150
Paragraph-level Simplification of Medical Texts
Devaraj, A., Marshall, I., Wallace, B., & Li, J. J. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.395 · Cited by 79
Metadata
- Authors
- Xu, W., Callison-Burch, C., & Napoles, C.
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2015-12
- Volume
- 3
- Pages
- 283-297
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W1746111881
- Cited by
- 369
- References
- 2
- FWCI
- 21.27
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Xu, W., Callison-Burch, C., & Napoles, C. (2015). Problems in Current Text Simplification Research: New Data Can Help. Transactions of the Association for Computational Linguistics. https://doi.org/10.1162/tacl_a_00139
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