Hostname: page-component-cb9f654ff-rkzlw Total loading time: 0 Render date: 2025-08-23T06:45:31.834Z Has data issue: false hasContentIssue false
  • English
  • Français

Horacio Saggion, Automatic Text Simplification. Synthesis lectures on human language technologies, April 2017. 137 pages, ISBN:1627058680 9781627058681

Review products

Horacio Saggion, Automatic Text Simplification. Synthesis lectures on human language technologies, April 2017. 137 pages, ISBN:1627058680 9781627058681

Published online by Cambridge University Press:  18 November 2019

Carolina Scarton
Carolina Scarton*
Affiliation:
Department of Computer Science, University of Sheffield, Sheffield, UK, Email: c.scarton@sheffield.ac.uk
Get access Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Information

Type
Book Review
Information
Natural Language Engineering , Volume 26 , Issue 4 , July 2020 , pp. 489 - 492
Copyright
© Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Aluísio, S.M. and Gasperin, C. (2010). Fostering digital inclusion and accessibility: The PorSimples project for simplification of Portuguese texts. In Proceedings of the NAACL HLT Young Investigators Workshop on Computational Approaches to Languages of the Americas, pp. 46–53. Association for Computational Linguistics.Google Scholar
Alva-Manchego, F., Joachim, B., Gustavo, P., Carolina, S. and Lucia, S. (2017). Learning how to simplify from explicit labeling of complex-simplified text pairs. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Taipei, Taiwan: Asian Federation of Natural Language Processing, pp. 295–305 Asian Federation of Natural Language Processing.Google Scholar
Carroll, J., Guido, M., Yvonne, C., Siobhan, D. and John, T. (1998). Practical simplification of English newspaper text to assist aphasic readers. In Proceedings of AAAI-98 Workshop on Integrating Artificial Intelligence and Assistive Technology, pp. 7–10, Madison, WI.Google Scholar
Coster, W. and Kauchak, D. (2011). Learning to simplify sentences using Wikipedia. In Proceedings of the Workshop on Monolingual Text-To-Text Generation, MTTG ’11, pages 1–9. Portland, OR: ACL.Google Scholar
Dong, Y., Zichao, L., Mehdi, R. and Cheung, J. C. K. (2019). EditNTS: an neural programmer-interpreter model for sentencesimplification through explicit editing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3393–3402, Florence, Italy. Association for Computational LinguisticsCrossRefGoogle Scholar
Guo, H., Ramakanth, P. and Mohit, B. (2018). Dynamic multi-level multi-task learning for sentence simplification. In Proceedings of the 27th International Conference on Computational Linguistics. Santa Fe, NM: Association for Computational Linguistics, pp. 462–476.Google Scholar
Jonnalagadda, S., Luis, T., Jörg, H., Chitta, B., and Graciela, G. (2009). Towards effective sentence simplification for automatic processing of biomedical text. In Proceedings of NAACL HLT 2009: Short Papers, pages 177–180, Boulder, CO. Association for Computational Linguistics.Google Scholar
Kriz, R.J.S., Marianna, A., Carolina, Z., Gaurav, K., Eleni, M. and Chris, C.-B. (2019). Complexity-weighted loss and diverse rerankingfor sentence simplification. In Proceedings of NAACL-HLT 2019, pp. 3137–3147, Florence, Italy. Association for Computational Linguistics.Google Scholar
Maynard, D., Valentin, T., Hamish, C., Cristian, U., Horacio, S., Kalina, B. and Yorick, W. (2002). Architectural elements of language engineering robustness. Natural Language Engineering 8, 257–274.CrossRefGoogle Scholar
Narayan, S. and Gardent, C. (2014). Hybrid simplification using deep semantics and machine translation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Baltimore, MD: Association for Computational Linguistics, pp. 435–445.CrossRefGoogle Scholar
Nisioi, S., Sanja, Š., Ponzetto, S.P. and Dinu, L.P. (2017). Exploring neural text simplification models. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Vancouver, Canada: Association for Computational Linguistics, pp. 85–91. Association for Computational Linguistics.CrossRefGoogle Scholar
Ong, E., Damay, J., Lojico, G., Lu, K. and Tarantan, D. (2007). Simplifying text in medical literature. Journal of Research in Science, Computing and Engineering 4, 37–47.Google Scholar
Paetzold, G. and Specia, H. (2015). LEXenstein: A Framework for Lexical Simplification Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Beijing, China. Association for Computational Linguistics. pp. 85–90.Google Scholar
Paetzold, G. H. (2016). Lexical Simplification for Non-native English Speakers. PhD Thesis, The University of Sheffield, Sheffield, UK.Google Scholar
Papineni, K., Roukos, S., Ward, T. and Zhu, W.-J. (2002). Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02. Philadelphia, PA: ACL, pp. 311–318.Google Scholar
Rello, L.D. (2014) A Text Accessibility Model for People with Dyslexia. PhD Thesis, Universitat Pompeu Fabra, Barcelona, Spain.Google Scholar
Saggion, H., Stajner, S., Bott, S., Mille, S., Rello, L. and Drndarevic, B. (2015). Making it simplext: implementation and evaluation of a text simplification system for Spanish. ACM Transactions on Accessible Computing (TACCESS) 6, 14.Google Scholar
Scarton, C. and Specia, L. (2018). Learning simplifications for specific target audiences. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Melbourne, Australia: Association for Computational Linguistics, pp. 712–718.CrossRefGoogle Scholar
Shardlow, M. (2014). A survey of automated text simplification. International Journal of Advanced Computer Science and Applications (IJACSA) Special Issue on Natural Language Processing 2014, 4(1), 58–70.Google Scholar
Siddharthan, A. (2006). Syntactic simplification and text cohesion. Research on Language and Computation 4, 77–109.CrossRefGoogle Scholar
Siddharthan, A. (2011). Text simplification using typed dependencies: a comparison of the robustness of different generation strategies. In Proceedings of the 13th European Workshop on Natural Language Generation, ENLG ’11. Stroudsburg, PA: Association for Computational Linguistics, pp. 2–11.Google Scholar
Specia, L., Jauhar, S.K. and Mihalcea, R. (2012). SemEval 2012 task 1: English lexical simplification. In Proceedings of the 1st Joint Conference on Lexical Computational Semantics, SemEval, pp. 347–355, Montréal, Canada. Association for Computational Linguistics.Google Scholar
Specia, L., Scarton, C. and Paetzold, G.H. (2018). Quality Estimation of Machine Translation. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, San Raphael, CA.Google Scholar
Stajner, S., Popovic, M. and Béchara, H. (2016). Quality estimation for text simplification. In Proceedings of the Workshop and Shared Task on Quality Assessment for Text Simplification (QATS). Pororoz, Slovenia.Google Scholar
Sulem, E., Abend, O. and Rappoport, A. (2018a). Bleu is not suitable for the evaluation of text simplification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 738–744.CrossRefGoogle Scholar
Sulem, E., Abend, O. and Rappoport, A. (2018b). Simple and effective text simplification using semantic and neural methods. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Melbourne, Australia: Association for Computational Linguistics, pp. 162–173.CrossRefGoogle Scholar
Sulem, E., Abend, O. and Rappoport, A. (2018c). Semantic structural evaluation for text simplification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). New Orleans, LA: Association for Computational Linguistics, pp. 685–696.CrossRefGoogle Scholar
Surya, S., Mishra, A., Laha, A., Jain, P. and Sankaranarayanan, K. (2019). Unsupervised neural text simplification. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2058–2068, Florence, Italy. Association for Computational Linguistics.CrossRefGoogle Scholar
Vu, T., Hu, B., Munkhdalai, T. and Yu, H. (2018). Sentence simplification with memory-augmented neural networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers). New Orleans, LA: Association for Computational Linguistics, pp. 79–85.CrossRefGoogle Scholar
Woodsend, K. and Lapata, M. (2011). Learning to simplify sentences with Quasi-synchronous grammar and integer programming. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Edinburgh, UK: Association for Computational Linguistics, pp. 409–420.Google Scholar
Xu, W., Napoles, C., Pavlick, E., Chen, Q. and Callison-Burch, C. (2016). Optimizing statistical machine translation for text simplification. Transactions of the Association for Computational Linguistics 4, 401–415.CrossRefGoogle Scholar
Zhang, X. and Lapata, M. (2017). Sentence simplification with deep reinforcement learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics, pp. 595–605.CrossRefGoogle Scholar
Zhao, S., Meng, R., He, D., Andi, S. and Bambang, P. (2018). Integrating transformer and paraphrase rules for sentence simplification. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 3164–3173, Brussels, Belgium. Association for Computational Linguistics.CrossRefGoogle Scholar
Zhu, Z., Bernhard, D. and Gurevych, I. (2010). A monolingual tree-based translation model for sentence simplification. In Proceedings of the 23rd International Conference on Computational Linguistics, COLING ’10, Stroudsburg, PA: Association for Computational Linguistics, pp. 1353–1361.Google Scholar