Using preliminary segmentation to increase the detail of semantic similarity measurement for scientific texts

M.S. Gavrilov

doi:10.18127/j20729472-202601-15

Using preliminary segmentation to increase the detail of semantic similarity measurement for scientific texts

research article UDC 004.92 Pages: 76-80 doi.org/10.18127/j20729472-202601-15

¹ V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences (Moscow, Russia)

Keywords: Semantic similarity scientific texts thematic segmentation language models text classification text vectorization document clustering

Abstract

A combined approach to assessing the semantic similarity of scientific texts is proposed, based on their preliminary thematic segmentation into meaningful blocks (e.g., «objectives», «methods», «results») followed by multidimensional comparison. An experimental improvement in document clustering quality compared to full-text analysis is demonstrated.

Received: 24.02.2026
Approved after review: 26.02.2026
Accepted: 10.03.2026

How to cite

Гаврилов М.С. Использование предварительной сегментации для повышения детальности измерения семантической близости научных текстов // Системы высокой доступности. 2026. Т. 22. № 1. С. 76−80. DOI: https://doi.org/10.18127/ j20729472-202601-15

References

Wang J., Dong Y. Measurement of text similarity: a survey. Information. 2020. V. 11. № 9. P. 421.
Govindaraju V., Ramanathan K. Similar document search andrecommendation. Journal of Emerging Technologies in Web Intelligence. 2012. V. 4. № 1. P. 84–93.
Caracciolo C., de Rijke M. Generating and retrieving text segments for focused access to scientific documents. Advances in Information Retrieval: 28th European Conference on IR Research, ECIR 2006. London, UK. April 10–12. 2006. P. 350–361.
Wan S., Lan Y., Guo J., Xu J., Pang L., Cheng X. A deep architecture for semantic matching with multiple positional sentence representations. Proceedings of the AAAI Conference on Artificial Intelligence. 2016. V. 30. № 1. https://doi.org/10.1609/aaai.v30i1.10342
Chernobaev I., Surkova A. Obzor metodov tematicheskoj segmentacii tekstovy`x danny`x. Informacionny`e sistemy` i texnologii IST-2018. 2018. S. 1079–1083.
Mishenin A. Tematicheskaya segmentaciya semanticheski odnorodny`x dokumentov. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaya matematika. Informa-tika. Processy` upravleniya. 2011. № 3. S. 127–133.
SciRus-tiny: model to obtain embeddings of scientific texts in russian and english. URL: https://huggingface.co/mlsa-iai-msu-lab/sci-rus-tiny (data obrashheniya 22.04.2025).
Liu et al. DeepSeek-V3 technical report. arXiv:2412.19437. 2024. https://doi.org/10.48550/arXiv. 2412.19437

[1] Wang J., Dong Y. Measurement of text similarity: a survey. Information. 2020. V. 11. № 9. P. 421.

[2] Govindaraju V., Ramanathan K. Similar document search andrecommendation. Journal of Emerging Technologies in Web Intelligence. 2012. V. 4. № 1. P. 84–93.

[3] Caracciolo C., de Rijke M. Generating and retrieving text segments for focused access to scientific documents. Advances in Information Retrieval: 28th European Conference on IR Research, ECIR 2006. London, UK. April 10–12. 2006. P. 350–361.

[4] Wan S., Lan Y., Guo J., Xu J., Pang L., Cheng X. A deep architecture for semantic matching with multiple positional sentence representations. Proceedings of the AAAI Conference on Artificial Intelligence. 2016. V. 30. № 1. https://doi.org/10.1609/aaai.v30i1.10342

[5] Chernobaev I., Surkova A. Obzor metodov tematicheskoj segmentacii tekstovy`x danny`x. Informacionny`e sistemy` i texnologii IST-2018. 2018. S. 1079–1083.

[6] Mishenin A. Tematicheskaya segmentaciya semanticheski odnorodny`x dokumentov. Vestnik Sankt-Peterburgskogo universiteta. Prikladnaya matematika. Informa-tika. Processy` upravleniya. 2011. № 3. S. 127–133.

[7] SciRus-tiny: model to obtain embeddings of scientific texts in russian and english. URL: https://huggingface.co/mlsa-iai-msu-lab/sci-rus-tiny (data obrashheniya 22.04.2025).

[8] Liu et al. DeepSeek-V3 technical report. arXiv:2412.19437. 2024. https://doi.org/10.48550/arXiv. 2412.19437

Abstract

How to cite

Related articles