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• LOW-RESOURCE ABSTRACTIVE TEXT SUMMARIZATION VIA CONTRASTIVE UNSUPERVISED LEARNING WITH A ROUGE-ORIENTED LOSS FUNCTION

  I. Е. Lysenko

  91-101

  2026-09-10

  Abstract ▼

  The relevance of this work is motivated by the fact that in specialized domains and low-resource languages obtaining a sufficient number of “document-summary” pairs for effective abstractive text summarization is expensive and often practically infeasible, whereas modern deep learning models require large labeled corpora and large volumes of text remain unused. The aim of this study is to develop a new training method for abstractive summarization models in low-resource (10-shot and 100-shot) settings that improves quality by fine-tuning the model on unlabeled data using unsupervised contrastive learning with input augmentation. The research tasks include designing a new contrastive loss function and comparing the proposed approach with existing methods of low-resource abstractive summarization. The methods and approaches comprise a new loss function for fine-tuning a transformer that includes a contrastive generative component based on a differentiable approximation of the ROUGE-3 metric. Two variants of the method are proposed – the sequential “DiffROUGE-seq” and the semi-supervised “DiffROUGE-sim”. BART-large is used as the base model, while input augmentations are generated by FLAN-T5-large in a zero-shot regime. Experiments are conducted on popular datasets AESLC, Gigaword, XSum, and Reddit. The proposed method achieves substantial improvements in ROUGE scores in successful cases, with average gains of 2.69 in the 10-shot setting and 2.13 in the 100-shot setting. In summary, the newly proposed low-resource abstractive summarization method that leverages both labeled and unlabeled data is significantly more effective in terms of ROUGE than existing approaches

• APPLICATION OF COMPUTER VISION TECHNOLOGIES IN VISUAL INFORMATION PROCESSING SYSTEMS

  О.B. Lebedev , R.I. Cherkasov

  254-276

  2025-11-10

  Abstract ▼

  This paper considers the application of artificial intelligence technologies, in particular computer vision, in visual information processing systems. A comprehensive analysis of neural network approaches to solving computer vision problems is carried out, including systematization of key types of problems: image classification, object detection and semantic segmentation. The architectural principles of convolutional neural networks are studied in detail with an emphasis on the mechanisms of spatial feature extraction through convolutional layers, optimization of data representation through pooling operations and feature transformation in fully connected layers. Particular attention is paid to the evolution of object detection methods, where the problem of model selection is considered as an extension of classification due to the integration of spatial coordinate regression, and an assessment of the effectiveness of detectors is carried out based on the IoU, Precision, Recall and F1-score metrics, demonstrating a fundamental trade-off between localization accuracy and processing speed. The YOLOv7 algorithm is presented as an optimal solution for real-time systems. Its architecture is based on splitting the input image into a grid of S×S cells with direct prediction of the bounding box parameters (center coordinates, width, height) and class probabilities for each cell, as well as the use of specialized layers (SPP, PANet) for multi-scale feature aggregation. The structure of the neural network confirms the effectiveness of the approach used, which ensures high performance without critically reducing accuracy in strategically important applications of video surveillance, autonomous systems, and augmented reality. A comparative study of one-stage and two-stage detectors was conducted with an assessment of their performance by key metrics. Particular attention is paid to the practical aspects of using computer vision technologies in real visual information processing systems.

• ALGORITHM FOR AUTOMATIC SELECTION OF INFORMATION PROTECTION MEASURES DEPENDING ON THE RESULTS OF THE VULNERABILITY SCANNER REPORT

  A.V. Anzina, A.D. Medvedeva, E.A. Emelyanov

  2021-02-13

  Abstract ▼

  Effective protection of information in an information system implies regular diagnostics and
  monitoring of the network, computers, and applications to detect possible problems in the security
  system. There are vulnerability scanners certified by the Federal Service for Technical and Export
  Control for security scanning. As a result of scanning, vulnerabilities of the information system
  can be identified, the elimination of which requires an immediate response, since attackers can
  take advantage of the vulnerability of the information system and carry out an attack. However,
  the selection of protection measures is a laborious process and requires a large amount of time,
  then the problem of automating the selection of information protection measures arises. The development
  of an algorithm for the automatic selection of information security measures is the main
  goal in automating the work process of an information security specialist. The main tasks in the
  development of the algorithm: selection of the fundamental characteristics of the vulnerability,
  generation of a list of protection measures taking into account the security class of the information
  system, comparison of protection measures with the selected characteristic. After analyzing the
  information about vulnerabilities, the main indicator is chosen the vulnerability vector, which
  includes the main metrics, the assessment of which allows the choice of protection measures. A set
  of information protection measures was compared to each metric by means of expert assessment.
  During the operation of the algorithm, the employee sets the vulnerability vector and the security
  class of the information system as input parameters and as a result receives a list of necessary
  protection measures. Thus, the automatic selection algorithm assumes a comparison of vulnerability
  metrics with information protection measures, which will allow an employee to quickly select
  measures based on the identified vulnerabilities.