Abstract
The general public tends to avoid reliable sources such as scientific literature due to their complex language and lacking background knowledge. Instead, they rely on shallow and derived sources on the web and in social media – often published for commercial or political incentives, rather than the informational value. Can text simplification help to remove some of these access barriers? This paper presents the CLEF 2023 SimpleText track tackling technical and evaluation challenges of scientific information access for a general audience. We provide appropriate reusable data and benchmarks for scientific text simplification, and promote novel research to reduce barriers in understanding complex texts. Our overall use-case is to create a simplified summary of multiple scientific documents based on a popular science query which provides a user with an accessible overview on this specific topic. The track has the following three concrete tasks. Task 1 (What is in, or out?): Selecting passages to include in a simplified summary. Task 2 (What is unclear?): Difficult concept identification and explanation. Task 3 (Rewrite this!): Text simplification - rewriting scientific text. The three tasks together form a pipeline of a scientific text simplification system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
August, T., Reinecke, K., Smith, N.A.: Generating scientific definitions with controllable complexity. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8298–8317 (2022)
Bott, S., Saggion, H.: An unsupervised alignment algorithm for text simplification corpus construction. In: Proceedings of the Workshop on Monolingual Text-To-Text Generation, pp. 20–26 (2011)
Cardon, R., Grabar, N.: French biomedical text simplification: when small and precise helps. In: Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, pp. 710–716. International Committee on Computational Linguistics (2020). https://www.aclweb.org/anthology/2020.coling-main.62
Chandrasekaran, M.K., et al.: Overview of the first workshop on scholarly document processing (SDP). In: Proceedings of the First Workshop on Scholarly Document Processing, pp. 1–6. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.sdp-1.1. https://aclanthology.org/2020.sdp-1.1/
Chen, P., Rochford, J., Kennedy, D.N., Djamasbi, S., Fay, P., Scott, W.: Automatic text simplification for people with intellectual disabilities. In: Artificial Intelligence Science and Technology, pp. 725–731. World Scientific (2016). https://www.worldscientific.com/doi/abs/10.1142/9789813206823_0091
Cruz, F., Coustaty, M., Augereau, O., Kise, K., Journet, N.: An interactive recommendation system for 2nd language vocabulary learning-vocabulometer 2.0. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 3, pp. 28–32. IEEE (2019)
Ermakova, L., et al.: Overview of SimpleText 2021 - CLEF workshop on text simplification for scientific information access. In: Candan, K.S., et al. (eds.) CLEF 2021. LNCS, vol. 12880, pp. 432–449. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85251-1_27
Ermakova, L., et al.: Automatic simplification of scientific texts: SimpleText lab at CLEF-2022. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13186, pp. 364–373. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99739-7_46
Ermakova, L., Ovchinnikova, I., Kamps, J., Nurbakova, D., Araújo, S., Hannachi, R.: Overview of the CLEF 2022 SimpleText task 2: complexity spotting in scientific abstracts. In: Faggioli et al. [12]
Ermakova, L., Ovchinnikova, I., Kamps, J., Nurbakova, D., Araújo, S., Hannachi, R.: Overview of the CLEF 2022 SimpleText task 3: query biased simplification of scientific texts. In: Faggioli et al. [12]
Ermakova, L., et al.: Overview of the CLEF 2022 SimpleText lab: automatic simplification of scientific texts. In: Barrón-Cedeño, A., et al. (eds.) CLEF 2022. LNCS, vol. 13390, pp. 470–494. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-13643-6_28
Faggioli, G., Ferro, N., Hanbury, A., Potthast, M. (eds.): Proceedings of the Working Notes of CLEF 2022: Conference and Labs of the Evaluation Forum. CEUR Workshop Proceedings (2022)
Gala, N., Tack, A., Javourey-Drevet, L., François, T., Ziegler, J.C.: Alector: a parallel corpus of simplified French texts with alignments of misreadings by poor and dyslexic readers. In: Language Resources and Evaluation for Language Technologies (LREC) (2020)
Grabar, N., Saggion, H.: Evaluation of automatic text simplification: where are we now, where should we go from here. In: Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1: conférence principale, pp. 453–463 (2022)
Inui, K., Fujita, A., Takahashi, T., Iida, R., Iwakura, T.: Text simplification for reading assistance: a project note. In: Proceedings of the Second International Workshop on Paraphrasing - Volume 16, PARAPHRASE 2003, pp. 9–16. ACL, USA (2003). https://doi.org/10.3115/1118984.1118986
Kochmar, E., Gooding, S., Shardlow, M.: Detecting multiword expression type helps lexical complexity assessment. In: LREC 2020: Proceedings of the 12th Conference on Language Resources and Evaluation (2020)
Monteiro, J., Aguiar, M., Araújo, S.: Using a pre-trained SimpleT5 model for text simplification in a limited corpus. In: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, 5–8 September 2022, Bologna, Italy. CEUR Workshop Proceedings, CEUR-WS.org (2022)
Mostert, F., Sampatsing, A., Spronk, M., Kamps, J.: University of Amsterdam at the CLEF 2022 SimpleText track. In: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, 5–8 September 2022, Bologna, Italy. CEUR Workshop Proceedings, CEUR-WS.org (2022)
Nakatani, M., Jatowt, A., Tanaka, K.: Easiest-first search: towards comprehension-based web search. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, pp. 2057–2060 (2009)
Navigli, R., Velardi, P.: Learning word-class lattices for definition and hypernym extraction. In: ACL, pp. 1318–1327 (2010)
Ravana, S.D., Moffat, A.: Score aggregation techniques in retrieval experimentation. In: Proceedings of the Twentieth Australasian Conference on Australasian Database, vol. 92, pp. 57–66 (2009)
Rello, L., Baeza-Yates, R., Bott, S., Saggion, H.: Simplify or help? Text simplification strategies for people with dyslexia. In: Proceedings of the 10th International Cross-Disciplinary Conference on Web Accessibility, pp. 1–10 (2013)
Rigouts Terryn, A., Hoste, V., Drouin, P., Lefever, E.: Termeval 2020: shared task on automatic term extraction using the annotated corpora for term extraction research (ACTER) dataset. In: 6th International Workshop on Computational Terminology (COMPUTERM 2020), pp. 85–94. European Language Resources Association (ELRA) (2020)
Robertson, S., Zaragoza, H., et al.: The probabilistic relevance framework: BM25 and beyond. Found. Trends® Inf. Retrieval 3(4), 333–389 (2009)
Rubio, A., Martínez, P.: HULAT-UC3M at SimpleText@CLEF-2022: scientific text simplification using BART. In: Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, 5–8 September 2022. CEUR Workshop Proceedings, CEUR-WS.org (2022)
SanJuan, E., Huet, S., Kamps, J., Ermakova, L.: Overview of the CLEF 2022 SimpleText task 1: passage selection for a simplified summary. In: Faggioli et al. [12]
Sheang, K.C., Saggion, H.: Controllable sentence simplification with a unified text-to-text transfer transformer. In: Proceedings of the 14th International Conference on Natural Language Generation, pp. 341–352 (2021)
Siddharthan, A.: An architecture for a text simplification system (2002). https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.1.9968 &rank=1
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: KDD 2008, pp. 990–998 (2008)
Xu, W., Callison-Burch, C., Napoles, C.: Problems in current text simplification research: new data can help. Trans. ACL 3, 283–297 (2015). https://www.mitpressjournals.org/doi/abs/10.1162/tacl_a_00139
Yimam, S.M., et al.: A report on the complex word identification shared task 2018. In: The 13th Workshop on Innovative Use of NLP for Building Educational Applications (NAACL2018 Workshops) (2018)
Zhang, X., Lapata, M.: Sentence simplification with deep reinforcement learning. In: EMNLP 2017: Conference on Empirical Methods in Natural Language Processing, pp. 584–594. Association for Computational Linguistics (2017)
Zhu, Z., Bernhard, D., Gurevych, I.: A monolingual tree-based translation model for sentence simplification. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), Beijing, China, pp. 1353–1361. Coling 2010 Organizing Committee (2010). https://www.aclweb.org/anthology/C10-1152
Štajner, S., Sheang, K.C., Saggion, H.: Sentence Simplification Capabilities of Transfer-Based Models (2022)
Acknowledgment
This track would not have been possible without the great support of numerous individuals. We want thank in particular Silvia Araujo, Patrice Bellot, Julien Boccou, Pierre De Loor, Radia Hannachi, Helen McCombie, Diana Nurbakova, Irina Ovchinnikov, and Léa Talec; the students of the Université de Bretagne Occidentale; and all the 2022 track participants for their great help in discussing and shaping the track, and in creating all the evaluation data and training data for 2023. We also thank the MaDICS (https://www.madics.fr/ateliers/simpletext/) research group and the French National Research Agency (project ANR-22-CE23-0019-01).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ermakova, L., SanJuan, E., Huet, S., Augereau, O., Azarbonyad, H., Kamps, J. (2023). CLEF 2023 SimpleText Track. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13982. Springer, Cham. https://doi.org/10.1007/978-3-031-28241-6_62
Download citation
DOI: https://doi.org/10.1007/978-3-031-28241-6_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-28240-9
Online ISBN: 978-3-031-28241-6
eBook Packages: Computer ScienceComputer Science (R0)