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A new network model for extracting text keywords

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Abstract

Text keywords are defined as meaningful and important words in a document, which provide a precise overview of its content and reflect the author’s writing intention. Keyword extraction methods have received a lot of attentions, among which is the network-based method. However, existing network-based keyword extraction methods only consider the connections between words in a document, while ignoring the impact of sentences. Since a sentence is made of many words, while words affect one another in a sentence, neglecting the influence of sentences will result in the loss of information. In this paper, we introduce a word network whose nodes represent words in a document, and define that any keyword extraction method based on a word network is called as a Word-net method. Then, we propose a new network model which considers the influence of sentences, and a new word-sentence method based on the new model. Experimental results demonstrate that our method outperforms the Word-net method, the classical term frequency-inverse document frequency (TF-IDF) method, most frequent method and TextRank method. The precision, recall, and F-measure of our result are respectively 7.95, 8.27 and 6.54% higher than the Word-net result, and the average precision of our result is 17.56% higher than the TF-IDF result. A two-way analysis of variance is employed to validate the empirical analysis, which indicates that keyword extraction methods and keyword numbers have statistically significant effects on the evaluation of metric values.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. U1434209), the National Key Research and Development Program of China (Grant No.2017YFB1201105) and the Research Foundation of State Key Laboratory of Railway Traffic Control and Safety (Grant No. RCS2018ZZ003).

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Authors and Affiliations

  1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Liu Yang, Keping Li & Hangfei Huang

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  1. Liu Yang
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  2. Keping Li
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Correspondence to Liu Yang.

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Yang, L., Li, K. & Huang, H. A new network model for extracting text keywords. Scientometrics 116, 339–361 (2018). https://doi.org/10.1007/s11192-018-2743-5

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