Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Using Collective Citing Sentences to Recognize Cited Text in Computational Linguistics Articles

  • Kang, In-Su (Dept. of Computer Science & Engineering, Kyungsung University)
  • Received : 2016.10.25
  • Accepted : 2016.11.14
  • Published : 2016.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a collective approach to cited text recognition by exploiting a set of citing text from different articles citing the same article. First, the proposed method gathers highly-ranked cited sentences from the cited article using a group of citing text to create a collective information of probable cited sentences. Then, such collective information is used to determine final cited sentences among highly-ranked sentences from similarity-based cited text recognition. Experiments have been conducted on the data set which consists of research articles from a computational linguistics domain. Evaluation results showed that the proposed method could improve the performance of similarity-based baseline approaches.

Keywords

References

  1. A. Abu-Jbara, and D. Radev, "Coherent Citation-Based Summarization of Scientific Papers," Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp. 500-509, 2011.
  2. K. Jaidka, M. Chandrasekaran, B. Elizalde, R. Jha, C. Jones, M. Kan, A. Khanna, D. Molla-Aliod, D. Radev, F. Ronzano, and H. Saggion, "The Computational Linguistics Summarization Pilot Task," Proceedings of Text Analysis Conference, Gaithersburg, USA, 2014.
  3. K. Jaidka, M. Chandrasekaran, S. Rustagi, and M. Kan, "Overview of the CL-SciSumm 2016 Shared Task," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 93-102, 2016.
  4. L. Moraes, S. Baki, R. Verma, and D. Lee, "University of Houston at CL-SciSumm 2016: SVMs with tree kernels and Sentence Similarity," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 113-121, 2016.
  5. L. Li, L. Mao, Y. Zhang, J. Chi, T. Huang, X. Cong, and H. Peng, "CIST System for CL-SciSumm 2016 Shared Task," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 156-167, 2016.
  6. P. Aggarwal, and R Sharma, "Lexical and Syntactic cues to identify Reference Scope of Citance," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 103-112, 2016.
  7. B. Malenfant, and G. Lapalme, "RALI System Description for CL-SciSumm 2016 Shared Task," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 146-155, 2016.
  8. S. Klampfl, A. Rexha, and R. Kern, "Identifying Referenced Text in Scientific Publications by Summarisation and Classification Techniques," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 122-131, 2016.
  9. Z. Cao, W. Li, and D. Wu, "PolyU at CL-SciSumm 2016," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 132-138, 2016.
  10. T. Nomoto, "NEAL: A Neurally Enhanced Approach to Linking Citation and Reference," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 168-174, 2016.
  11. K. Lu, J. Mao, G. Li, and J. Xu, "Recognizing Reference Spans and Classifying their Discourse Facets," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 139-145, 2016.
  12. H. Saggion, A. AbuRa'Ed, and F. Ronzano, "Trainable Citation-enhanced Summarization of Scientific Articles," Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries, pp. 175-186, 2016.
  13. Q. Mei, and C. Zhai, "Generating Impact-Based Summaries for Scientific Literature," Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp. 816-824, 2008.
  14. P. Jaccard, "Nouvelles recherches sur la distribution florale," Bull. Soc. Vaud. Sci. Nat. Vol. 44, pp. 223-270, 1908.
  15. O. Hassanzadeh, M. Sadoghi, and R. Miller, "Accuracy of Approximate String Joins Using Grams," Proceedings of the Fifth International Workshop on Quality in Databases, pp. 11-18, 2007.
  16. M. Porter, "An algorithm for suffix stripping," Program, Vol. 14, No. 3, pp. 130-137, 1980. https://doi.org/10.1108/eb046814
  17. C. Manning, P. Raghavan, and H. Schutze, "Introduction to information retrieval," Cambridge University Press, 2008.
  18. K. Jaidka, M. Chandrasekaran, S. Rustagi, and M. Kan, "The Computational Linguistics Summarization Pilot Task @ BIRNDL 2016," CL-SciSumm2016_sildedeck.pdf in https://github.com/WING-NUS/scisumm-corpus, 2016.