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TopicLPRank: a keyphrase extraction method based on improved TopicRank

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Abstract

We present a keyphrase extraction algorithm named TopicLPRank in this paper, which is an improved TopicRank algorithm. Different from the TopicRank which only uses the relative distance information of the text, we think that the length and absolute position of the text candidate keyphrases also have a certain influence on the results of the model for extraction keyphrases. Therefore, the proposed TopicLPRank incorporates these two factors on the basis of the TopicRank. The experimental results show that adding the location information and length information of candidate keyphrases can, respectively, increase the F-Score of the model by around 2.7\(\%\) points and 1.7\(\%\) points, which is equivalent to an increase of 19.6 and 12.3\(\%\) compared with the TopicRank. At the same time, the fusion of the length and location information of the candidate keyphrase can increase the F-Score by around 3.5 percentage points, which is equivalent to an increase of 25.21\(\%\) compared with the TopicRank in the dataset NUS.

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All data included in this study are available upon request by contact with the corresponding author.

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Acknowledgments

This work was support by National Natural Science Foundation of China (grant number: 62077023 and 61937001), and National Key R &D Program of China titled with the Large-Scale Longitudinal and Cross-Sectional Study of Student Development (grant number: 2021YFC3340800).

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Authors and Affiliations

  1. National Engineering Research Center for E-Learning, Huazhong Normal University, Wuhan, China

    Shengbin Liao & Zhangxiong zheng

  2. National Engineering Laboratory for Educational Big Data, Huazhong Normal University, Wuhan, China

    Zongkai Yang

  3. Wuhan Vocational College of Software and Engineering, Wuhan, China

    Qingzhou Liao

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  1. Shengbin Liao
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  2. Zongkai Yang
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  3. Qingzhou Liao
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  4. Zhangxiong zheng
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Correspondence to Shengbin Liao.

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Liao, S., Yang, Z., Liao, Q. et al. TopicLPRank: a keyphrase extraction method based on improved TopicRank. J Supercomput 79, 9073–9092 (2023). https://doi.org/10.1007/s11227-022-05022-0

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