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Identifying and Representing Knowledge Delta in Scientific Literature

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Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13982))

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Abstract

The process of continuously keeping up to date with the state-of-the-art on a specific research topic is a challenging task for researchers not least due to the rapid increase of published research. In this research proposal, we define the term Knowledge Delta (KD) between scientific articles which refers to the differences between pairs of research articles that are similar in some aspects. We propose a three-phase research methodology to identify and represent the KD between articles. We intend to explore the effect of applying different text representations on extracted facts from scientific articles on the downstream task of KD identification.

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Notes

  1. 1.

    https://www.connectedpapers.com/.

References

  1. Abdulahhad, K.: Concept embedding for information retrieval. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 563–569. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-76941-7_45

    Chapter  Google Scholar 

  2. Abu-Jbara, A., Radev, D.: Coherent citation-based summarization of scientific papers. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 500–509 (2011)

    Google Scholar 

  3. Accuosto, P., Neves, M., Saggion, H.: Argumentation mining in scientific literature: from computational linguistics to biomedicine. In: Frommholz, I., Mayr, P., Cabanac, G., Verberne, S. (eds.) BIR 2021: 11th International Workshop on Bibliometric-Enhanced Information Retrieval; 1 April 2021, CEUR 2021, Lucca, Italy, pp. 20–36. CEUR Workshop Proceedings, Aachen (2021)

    Google Scholar 

  4. Accuosto, P., Saggion, H.: Transferring knowledge from discourse to arguments: a case study with scientific abstracts. In: Stein, B., Wachsmuth, H. (eds.) Proceedings of the 6th Workshop on Argument Mining, 1 August 2019, Florence, Italy, pp. 41–51. Association for Computational Linguistics. ACL (Association for Computational Linguistics), Stroudsburg (2019)

    Google Scholar 

  5. Accuosto, P., Saggion, H.: Mining arguments in scientific abstracts with discourse-level embeddings. Data Knowl. Eng. 129, 101840 (2020)

    Article  Google Scholar 

  6. Bedi, M., Pandey, T., Bhatia, S., Chakraborty, T.: Why did you not compare with that? Identifying papers for use as baselines. In: Hagen, M., et al. (eds.) Advances in Information Retrieval, pp. 51–64. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99736-6_4

    Chapter  Google Scholar 

  7. Beel, J., Gipp, B., Langer, S., Breitinger, C.: Research-paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2015). https://doi.org/10.1007/s00799-015-0156-0

    Article  Google Scholar 

  8. Bhagavatula, C., Feldman, S., Power, R., Ammar, W.: Content-based citation recommendation. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 238–251. Association for Computational Linguistics, New Orleans, Louisiana, June 2018. https://doi.org/10.18653/v1/N18-1022, https://aclanthology.org/N18-1022

  9. Chakraborty, T., Krishna, A., Singh, M., Ganguly, N., Goyal, P., Mukherjee, A.: FeRoSA: a faceted recommendation system for scientific articles. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J.Z., Wang, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 528–541. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31750-2_42

    Chapter  Google Scholar 

  10. Chakraborty, T., Narayanam, R.: All fingers are not equal: intensity of references in scientific articles. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1348–1358. Association for Computational Linguistics, Austin, Texas, November 2016. https://doi.org/10.18653/v1/D16-1142, https://aclanthology.org/D16-1142

  11. Chandrasekaran, M.K., Feigenblat, G., Hovy, E., Ravichander, A., Shmueli-Scheuer, M., De Waard, A.: Overview and insights from scientific document summarization shared tasks 2020: CL-SciSumm, LaySumm and LongSumm. In: Proceedings of the First Workshop on Scholarly Document Processing (SDP 2020) (2020)

    Google Scholar 

  12. Chandrasekaran, M.K., Yasunaga, M., Radev, D., Freitag, D., Kan, M.Y.: Overview and results: CL-SciSumm shared task 2019. arXiv preprint arXiv:1907.09854 (2019)

  13. Cohan, A., Ammar, W., van Zuylen, M., Cady, F.: Structural scaffolds for citation intent classification in scientific publications. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 3586–3596. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https://doi.org/10.18653/v1/N19-1361, https://aclanthology.org/N19-1361

  14. Ding, Y., Zhang, G., Chambers, T., Song, M., Wang, X., Zhai, C.: Content-based citation analysis: the next generation of citation analysis. J. Am. Soc. Inf. Sci. 65(9), 1820–1833 (2014)

    Google Scholar 

  15. Dong, C., Schäfer, U.: Ensemble-style self-training on citation classification. In: Proceedings of 5th International Joint Conference on Natural Language Processing, pp. 623–631. Asian Federation of Natural Language Processing, Chiang Mai, Thailand, November 2011. https://aclanthology.org/I11-1070

  16. El-Ebshihy, A., Ningtyas, A.M., Andersson, L., Piroi, F., Rauber, A.: A platform for argumentative zoning annotation and scientific summarization. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM 2022, pp. 4843–4847. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3511808.3557193

  17. Fricke, S.: Semantic scholar. J. Med. Libr. Assoc. JMLA 106(1), 145 (2018)

    Google Scholar 

  18. Jacsó, P.: Google Scholar: the pros and the cons. Online Inf. Rev. 29(2), 208–214 (2005)

    Article  Google Scholar 

  19. Jaidka, K., et al.: The computational linguistics summarization pilot task. In: Proceedings of Text Analysis Conference, Gaithersburg, USA (2014)

    Google Scholar 

  20. Jaidka, K., Chandrasekaran, M.K., Rustagi, S., Kan, M.Y.: Overview of the CL-SciSumm 2016 shared task. In: Proceedings of the Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL), pp. 93–102 (2016)

    Google Scholar 

  21. Jaidka, K., Yasunaga, M., Chandrasekaran, M.K., Radev, D., Kan, M.Y.: The CL-SciSumm shared task 2018: results and key insights. arXiv preprint arXiv:1909.00764 (2019)

  22. Jeong, C., Jang, S., Park, E., Choi, S.: A context-aware citation recommendation model with BERT and graph convolutional networks. Scientometrics 124(3), 1907–1922 (2020). https://doi.org/10.1007/s11192-020-03561-y

    Article  Google Scholar 

  23. Ji, S., Pan, S., Cambria, E., Marttinen, P., Yu, P.S.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Netw. Learn. Syst. 33(2), 494–514 (2022). https://doi.org/10.1109/tnnls.2021.3070843

  24. Jurgens, D., Kumar, S., Hoover, R., McFarland, D., Jurafsky, D.: Measuring the evolution of a scientific field through citation frames. Trans. Assoc. Comput. Linguist. 6, 391–406 (2018). https://doi.org/10.1162/tacl_a_00028, https://aclanthology.org/Q18-1028

  25. Lev, G., Shmueli-Scheuer, M., Herzig, J., Jerbi, A., Konopnicki, D.: TalkSumm: a dataset and scalable annotation method for scientific paper summarization based on conference talks. CoRR abs/1906.01351 (2019). http://arxiv.org/abs/1906.01351

  26. Liu, H.: Automatic argumentative-zoning using Word2vec. CoRR abs/1703.10152 (2017). http://arxiv.org/abs/1703.10152

  27. Pride, D., Knoth, P.: An authoritative approach to citation classification, pp. 337–340. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3383583.3398617

  28. Siddiqi, S., Sharan, A.: Keyword and keyphrase extraction techniques: a literature review. Int. J. Comput. Appl. 109, 18–23 (2015). https://doi.org/10.5120/19161-0607

  29. Stevens, M.E., Giuliano, V.E., Garfield, E.: Can citation indexing be automated ? (1964)

    Google Scholar 

  30. Tang, J., Zhang, J.: A discriminative approach to topic-based citation recommendation. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.B. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 572–579. Springer, Cham (2009). https://doi.org/10.1007/978-3-642-01307-2_55

    Chapter  Google Scholar 

  31. Teufel, S., Carletta, J., Moens, M.: An annotation scheme for discourse-level argumentation in research articles. In: Ninth Conference of the European Chapter of the Association for Computational Linguistics (1999)

    Google Scholar 

  32. Teufel, S., Moens, M.: Summarizing scientific articles: experiments with relevance and rhetorical status. Comput. Linguist. 28(4), 409–445 (2002)

    Article  Google Scholar 

  33. Teufel, S., Siddharthan, A., Batchelor, C.: Towards domain-independent argumentative zoning: evidence from chemistry and computational linguistics. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 1493–1502 (2009)

    Google Scholar 

  34. Teufel, S., Siddharthan, A., Tidhar, D.: Automatic classification of citation function. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 103–110. Association for Computational Linguistics, Sydney, Australia, July 2006. https://aclanthology.org/W06-1613

  35. Teufel, S., et al.: Argumentative zoning: Information extraction from scientific text. Ph.D. thesis, Citeseer (1999)

    Google Scholar 

  36. Wu, J., et al.: CiteSeerX: AI in a digital library search engine. AI Mag. 36(3), 35–48 (2015)

    Google Scholar 

  37. Yang, L., et al.: A LSTM based model for personalized context-aware citation recommendation. IEEE Access 6, 59618–59627 (2018)

    Article  Google Scholar 

  38. Yasunaga, M., et al.: ScisummNet: a large annotated corpus and content-impact models for scientific paper summarization with citation networks. In: Proceedings of AAAI 2019 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Research Studio Austria, Vienna, Austria

    Alaa El-Ebshihy

  2. Technische Universität Wien, Vienna, Austria

    Alaa El-Ebshihy

  3. Alexandria University, Alexandria, Egypt

    Alaa El-Ebshihy

Authors
  1. Alaa El-Ebshihy
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Correspondence to Alaa El-Ebshihy .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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El-Ebshihy, A. (2023). Identifying and Representing Knowledge Delta in Scientific Literature. 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_49

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  • DOI: https://doi.org/10.1007/978-3-031-28241-6_49

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