Text Simplification for Scientific Information Access

1. “2019-nCoV” OR ... Publication Year: 2020 in Publications - Dimensions. https://covid-19.dimensions.ai/

2. Background knowledge. https://www.thefreedictionary.com/background+knowledge

3. Altbach, P.G., Wit, H.D.: Too much academic research is being published, July 2018. https://www.universityworldnews.com/post.php?story=20180905095203579

4. Augenstein, I., Das, M., Riedel, S., Vikraman, L., McCallum, A.: Semeval 2017 task 10: scienceie-extracting keyphrases and relations from scientific publications. arXiv preprint arXiv:1704.02853 (2017)

5. Barzilay, R., Elhadad, N., McKeown, K.R.: Inferring strategies for sentence ordering in multidocument news summarization. J. Artif. Intell. Res. 17, 35–55 (2002)

   Google Scholar 

6. Bellot, P., et al.: Overview of INEX. In: Information Access Evaluation. Multilinguality, Multimodality, and Visualization - 4th International Conference of the CLEF Initiative, CLEF 2013, Valencia, Spain, 23–26 September 2013. Proceedings, pp. 269–281 (2013)

   Google Scholar 

7. Bellot, P., Moriceau, V., Mothe, J., SanJuan, E., Tannier, X.: INEX tweet contextualization task: evaluation, results and lesson learned. Inf. Process. Manage. 52(5), 801–819 (2016). https://doi.org/10.1016/j.ipm.2016.03.002

8. Chae, J., Nenkova, A.: Predicting the fluency of text with shallow structural features: case studies of machine translation and human-written text. In: Proceedings of the 12th Conference of the European Chapter of the ACL, pp. 139–147 (2009)

   Google Scholar 

9. Chall, J.S., Dale, E.: Readability revisited: The new Dale-Chall readability. Brookline Books, Cambridge (1995)

   Google Scholar 

10. Collins-Thompson, K., Callan, J.: A language modeling approach to predicting reading difficulty. In: Proceedings of HLT/NAACL, vol. 4 (2004)

    Google Scholar 

11. Coster, W., Kauchak, D.: Simple English Wikipedia: a new text simplification task. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 665–669 (2011)

    Google Scholar 

12. Cram, D., Daille, B.: Terminology extraction with term variant detection. In: Proceedings of ACL-2016 System Demonstrations, Berlin, Germany, pp. 13–18. Association for Computational Linguistics, August 2016. https://doi.org/10.18653/v1/P16-4003, https://www.aclweb.org/anthology/P16-4003

13. Ermakova, L., Cossu, J.V., Mothe, J.: A survey on evaluation of summarization methods. Inf. Process. Manage. 56(5), 1794–1814 (2019). https://doi.org/10.1016/j.ipm.2019.04.001, http://www.sciencedirect.com/science/article/pii/S0306457318306241

14. Ermakova, L., Goeuriot, L., Mothe, J., Mulhem, P., Nie, J.-Y., SanJuan, E.: CLEF 2017 microblog cultural contextualization lab overview. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 304–314. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65813-1_27

    Chapter  Google Scholar 

15. Ermakova, L., Mothe, J., Firsov, A.: A metric for sentence ordering assessment based on topic-comment structure (short paper). In: ACM SIGIR Special Interest Group on Information Retrieval (SIGIR), Tokyo, Japan, 07/08/2017-11/08/2017 (2017). selection rate 30

    Google Scholar 

16. Fecher, B., Friesike, S.: Open science: one term, five schools of thought. In: Bartling, S., Friesike, S. (eds.) Opening Science, pp. 17–47. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-00026-8_2

    Chapter  Google Scholar 

17. Feng, L., Jansche, M., Huenerfauth, M., Elhadad, N.: A comparison of features for automatic readability assessment. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. COLING 2010, Stroudsburg, PA, USA, pp. 276–284, Association for Computational Linguistics (2010). http://dl.acm.org/citation.cfm?id=1944566.1944598

18. Filippova, K., Altun, Y.: Overcoming the lack of parallel data in sentence compression. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1481–1491 (2013)

    Google Scholar 

19. Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32(3), p221–233 (1948)

    Article  Google Scholar 

20. Fry, E.: A readability formula for short passages. J. Read. 8(594–597), 33 (1990)

    Google Scholar 

21. Fry, E.: The Varied Uses of Readability Measurement, April 1986

    Google Scholar 

22. Heilman, M., Collins-Thompson, K., Eskenazi, M.: An analysis of statistical models and features for reading difficulty prediction. In: Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications. EANL 2008, Stroudsburg, PA, USA, pp. 71–79. Association for Computational Linguistics (2008). http://dl.acm.org/citation.cfm?id=1631836.1631845

23. Hoffart, J., et al.: Robust disambiguation of named entities in text. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 782–792 (2011)

    Google Scholar 

24. Jarreau, P.B., Porter, L.: Science in the social media age: profiles of science blog readers. J. Mass Commun. Quart. 95(1), 142–168 (2018). https://doi.org/10.1177/1077699016685558, publisher: SAGE Publications Inc

25. Ladyman, J., Lambert, J., Wiesner, K.: What is a complex system? European J. Philos. Sci. 3(1), 33–67 (2013). https://doi.org/10.1007/s13194-012-0056-8

26. Leroy, G., Endicott, J.E., Kauchak, D., Mouradi, O., Just, M.: User evaluation of the effects of a text simplification algorithm using term familiarity on perception, understanding, learning, and information retention. J. Medical Internet Res. 15(7), e144 (2013)

    Google Scholar 

27. McCarthy, P.M., Guess, R.H., McNamara, D.S.: The components of paraphrase evaluations. Behav. Res. Methods 41(3), 682–690 (2009). https://doi.org/10.3758/BRM.41.3.682

28. Molek-Kozakowska, K.: Communicating environmental science beyond academia: Stylistic patterns of newsworthiness in popular science journalism. Disc. Commun. 11(1), 69–88 (2017). https://doi.org/10.1177/1750481316683294

29. Mutton, A., Dras, M., Wan, S., Dale, R.: Gleu: automatic evaluation of sentence-level fluency. In: ACL 2007, pp. 344–351 (2007)

    Google Scholar 

30. O’Reilly, T., Wang, Z., Sabatini, J.: How much knowledge is too little? When a lack of knowledge becomes a barrier to comprehension. Psychol. Sci. (2019). https://doi.org/10.1177/0956797619862276, https://journals.sagepub.com/doi/10.1177/0956797619862276, publisher: SAGE PublicationsSage CA: Los Angeles, CA

31. Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 311–318 (2002)

    Google Scholar 

32. Petersen, S.E., Ostendorf, M.: A machine learning approach to reading level assessment. Comput. Speech Lang. 23(1), 89–106 (2009). https://doi.org/10.1016/j.csl.2008.04.003, http://dx.doi.org/10.1016/j.csl.2008.04.003

33. Pitler, E., Nenkova, A.: Revisiting readability: A unified framework for predicting text quality (2008)

    Google Scholar 

34. Rao, S., Tetreault, J.: Dear sir or madam, may i introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 129–140 (2018)

    Google Scholar 

35. Shi, H., Revithis, S., Chen, S.S.: An agent enabling personalized learning in e-learning environments. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems: Part 2. AAMAS 2002, New York, NY, USA, pp. 847–848. Association for Computing Machinery, July 2002. https://doi.org/10.1145/544862.544941

36. Si, L., Callan, J.: A statistical model for scientific readability. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, CIKM 2001, pp. 574–576, New York, NY, USA. ACM (2001). https://doi.org/10.1145/502585.502695, http://doi.acm.org/10.1145/502585.502695

37. Stenner, A.J., Horablin, I., Smith, D.R., Smith, M.: The Lexile Framework. Metametrics, Durham, NC (1988)

    Google Scholar 

38. Stenner, A., Horabin, I., Smith, D.R., Smith, M.: The Lexile Framework. MetaMetrics, Durham, NC (1988)

    Google Scholar 

39. Sulem, E., Abend, O., Rappoport, A.: 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), pp. 685–696 (2018)

    Google Scholar 

40. Søe, S.O.: Algorithmic detection of misinformation and disinformation: Gricean perspectives. J. Doc. 74(2), 309–332 (2018). https://doi.org/10.1108/JD-05-2017-0075, publisher: Emerald Publishing Limited

41. Tavernier, J., Bellot, P.: Combining relevance and readability for INEX 2011 question-answering track, pp. 185–195 (2011)

    Google Scholar 

42. Wan, S., Dale, R., Dras, M.: Searching for grammaticality: propagating dependencies in the Viterbi algorithm. In: Proceedings of the Tenth European Workshop on Natural Language Generation (2005)

    Google Scholar 

43. Wubben, S., van den Bosch, A., Krahmer, E.: Sentence simplification by monolingual machine translation. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1015–1024 (2012)

    Google Scholar 

44. Xu, W., Napoles, C., Pavlick, E., Chen, Q., Callison-Burch, C.: Optimizing statistical machine translation for text simplification. Trans. Assoc. Comput. Linguist. 4, 401–415 (2016)

    Article  Google Scholar 

45. Zakaluk, B.L., Samuels, S.J.: Readability: its past, present, and future. International Reading Association, 800 Barksdale Rd (1988). https://eric.ed.gov/?id=ED292058

46. Zwarts, S., Dras, M.: Choosing the right translation: a syntactically informed classification approach. In: Proceedings of the 22nd International Conference on Computational Linguistics, pp. 1153–1160 (2008)

    Google Scholar 

Download references