Ranking-Based Cited Text Identification with Highway Networks

  • Shiyan OuEmail author
  • Hyonil Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12051)


In recent years, content-based citation analysis (CCA) has attracted great attention, which focuses on citation texts within full-text scientific articles to analyze the meaning of each citation. However, citation texts often lack the appropriate evidence and context from cited papers and are sometimes even inaccurate. Thus it is necessary to identify the corresponding cited text from a cited paper and examine which part of the content of the paper was cited in a citation. In this study, we proposed a novel ranking-based method to identify cited texts. This method contains two stages: similarity-based unsupervised ranking and deep learning-based supervised ranking. A novel listwise ranking model was developed with the use of 36 similarity features and 11 section position features. Firstly, top-5 sentences were selected for each citation text according to a modified Jaccard similarity metric. Then the selected sentences were ranked using the trained listwise ranking model, and top-2 sentences were selected as cited sentences. The experiments showed that the proposed method outperformed other classification-based and voting-based identification methods on the test set of the CL-SciSumm 2017.


Content-based citation analysis Cited text identification Listwise ranking Text similarity Deep learning 



This paper is one of the research outputs of the project supported by the State Key Program of National Social Science Foundation of China (Grant No. 17ATQ001).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information ManagementNanjing UniversityNanjingChina

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