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Soojin Jang

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AAAI Conference 2025 System Paper

SummPilot: Bridging Efficiency and Customization for Interactive Summarization System

  • JungMin Yun
  • Juhwan Choi
  • Kyohoon Jin
  • Soojin Jang
  • Jinhee Jang
  • YoungBin Kim

This paper incorporates the efficiency of automatic summarization and addresses the challenge of generating personalized summaries tailored to individual users' interests and requirements. To tackle this challenge, we introduce SummPilot, an interaction-based customizable summarization system. SummPilot leverages a large language model to facilitate both automatic and interactive summarization. Users can engage with the system to understand document content and personalize summaries through interactive components such as semantic graphs, entity clustering, and explainable evaluation. Our demo and user studies demonstrate SummPilot's adaptability and usefulness for customizable summarization.

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EAAI Journal 2023 Journal Article

Weakly supervised semantic segmentation via Graph RecalibratiOn with Scaling Weight uNit

  • Soojin Jang
  • JuneHyoung Kwon
  • Kyohoon Jin
  • YoungBin Kim

Weakly supervised semantic segmentation (WSSS) has garnered considerable attention for its efficacy in generating pixel-level labels using weak labels. Class activation maps (CAMs) are utilized by WSSS to generate pseudo-masks from image-level labels. However, these CAMs primarily focus on the most discriminative features of an object, while less discriminative features may be ignored or unidentified. Due to co-occurring pixels, it may also be impossible to distinguish between the foreground and background. In this paper, we propose a method referred to as Graph RecalibratiOn with Scaling Weight uNit (GROWN) to address these challenges. It illustrates the relation between local and global features by utilizing graph structure. Adaptively representing the image’s semantic features is possible by scaling weights that aggregate contextual features. The proposed method successfully captures long-range dependencies and extracts contextual features to improve the pseudo-mask quality. As a result, the proposed method can predict pixel-level labels effectively. The datasets PASCAL VOC 2012 and MS COCO were utilized in the experiments. GROWN outperforms state-of-the-art WSSS methods that employ image-level labels, as demonstrated by the results.

Details DOI
v2026.09.13