Abstract
Everyone agrees on the importance of objective scientific information. However, relevant scientific documents tend to be inherently difficult to find and understand either because of intricate terminology or the potential absence of prior knowledge among their readers. Can we improve accessibility for everyone? This paper introduces the SimpleText Track at CLEF 2024, addressing the technical and evaluation challenges associated with making scientific information accessible to a wide audience, including students and non-experts. We provide appropriate reusable data and benchmarks for scientific text summarization and simplification. The CLEF 2024 SimpleText track is based on four interrelated tasks: Task 1 Content Selection: Retrieving Passages to Include in a Simplified Summary. Task 2 Complexity Spotting: Identifying and Explaining Difficult Concepts. Task 3 Text Simplification: Simplify Scientific Text. Task 4 SOTA?: Tracking the State-of-the-Art in Scholarly Publications.
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
Notes
- 1.
A joined effort with Scholarly Document Processing https://sdproc.org/2024/.
- 2.
- 3.
References
Aliannejadi, M., Faggioli, G., Ferro, N., Vlachos, M. (eds.): Working Notes of CLEF 2023: Conference and Labs of the Evaluation Forum, CEUR Workshop Proceedings, vol. 3497, CEUR-WS.org (2023). http://ceur-ws.org/Vol-3497
Ermakova, L., Azarbonyad, H., Bertin, S., Augereau, O.: Overview of the CLEF 2023 SimpleText Task 2: difficult concept identification and explanation. In: [1]. https://ceur-ws.org/Vol-3497/paper-239.pdf
Ermakova, L., et al.: Text Simplification for Scientific Information Access: CLEF 2021 SimpleText Workshop. In: Advances in Information Retrieval - 43nd European Conference on IR Research, ECIR 2021, Lucca, Italy, March 28 - April 1, 2021, Proc., Lucca, Italy (2021)
Ermakova, L., Bertin, S., McCombie, H., Kamps, J.: Overview of the CLEF 2023 SimpleText Task 3: Scientific text simplification. In: [1]. https://ceur-ws.org/Vol-3497/paper-240.pdf
Ermakova, L., SanJuan, E., Huet, S., Azarbonyad, H., Augereau, O., Kamps, J.: Overview of the CLEF 2023 SimpleText Lab: automatic simplification of scientific texts. In: Arampatzis, A., et al. (eds.) CLEF’23: Proceedings of the Fourteenth International Conference of the CLEF Association. LNCS. Springer (2023). https://doi.org/10.1007/978-3-031-42448-9_30
Ermakova, L., et al.: Overview of the CLEF 2022 SimpleText lab: automatic simplification of scientific texts. In: Barrón-Cedeño, A., et al. (eds.) CLEF’22: Proceedings of the Thirteenth International Conference of the CLEF Association. LNCS. Springer (2022)
Kabongo, S., D’Souza, J., Auer, S.: Automated mining of leaderboards for empirical ai research. In: Towards Open and Trustworthy Digital Societies: 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Virtual Event, December 1–3, 2021, Proceedings 23, pp. 453–470. Springer (2021)
Kabongo, S., D’Souza, J., Auer, S.: Orkg-leaderboards: a systematic workflow for mining leaderboards as a knowledge graph. arXiv preprint arXiv:2305.11068 (2023)
Kabongo, S., D’Souza, J., Auer, S.: Zero-shot entailment of leaderboards for empirical ai research. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2023 (2023)
Navigli, R., Velardi, P.: Learning word-class lattices for definition and hypernym extraction. In: ACL, pp. 1318–1327 (2010)
SanJuan, E., Huet, S., Kamps, J., Ermakova, L.: Overview of the CLEF 2023 simpletext task 1: passage selection for a simplified summary. In: [1]. https://ceur-ws.org/Vol-3497/paper-238.pdf
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: ArnetMiner: extraction and mining of academic social networks. In: KDD’08, 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). ISSN 2307–387X. https://www.mitpressjournals.org/doi/abs/10.1162/tacl_a_00139
Acknowledgments
This track would not have been possible without the great support of numerous individuals. We want to thank in particular the colleagues and the students who participated in data construction, evaluation and reviewing. 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). SimpleText’s SOTA Task is jointly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number: NFDI4DataScience (460234259) and the German BMBF project SCINEXT (01lS22070).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ermakova, L. et al. (2024). CLEF 2024 SimpleText Track. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14613. Springer, Cham. https://doi.org/10.1007/978-3-031-56072-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-56072-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56071-2
Online ISBN: 978-3-031-56072-9
eBook Packages: Computer ScienceComputer Science (R0)