Publication details

Comparative Analysis of Community Detection and Transformer-Based Approaches for Topic Clustering of Scientific Papers

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BRETSKO Daniel BELY Aliaksandr SOBOLEVSKY Stanislav

Year of publication 2023
Type Article in Proceedings
Conference 23rd International Conference on Computational Science and Its Applications , ICCSA 2023
MU Faculty or unit

Faculty of Science

Keywords Network analysis; NLP; Topic clustering; Community detection; Sentence-transformers
Description We are solving the topic clustering problem, where we need to categorize papers with initially available subjects into more consistent and higher-level topics. We approach the task from two perspectives, one is the traditional network science, where we perform community detection on a subject network with the use of Combo algorithm, and the second is the transformer-based top2vec algorithm which uses sentence-transformer to embed the content of the papers. The comparison between the two approaches was conducted using a dataset of scientific papers on computer science and mathematics collected from the SCOPUS database, and different coherence scores were used as a measure of performance. The results showed that the community detection Combo algorithm was able to achieve a similar coherence score to the transformer-based top2vec. The findings suggest that community detection may be a viable alternative for topic clustering when one has predefined topics, especially when a high coherence score and fast processing time are desired. The paper also discusses the potential advantages and limitations of using Combo for topic clustering and the potential for future work in this area.
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