Proposal Report for the 2nd SciCAP Competition 2024
원문 초록
In this paper, we propose a method for document summarization using auxiliary information. This approach effectively summarizes descriptions related to specific images, tables, and appendices within lengthy texts. Our experiments demonstrate that leveraging high-quality OCR data and initially extracted information from the original text enables efficient summarization of the content related to described objects. Based on these findings, we enhanced popular text generation model models by incorporating additional auxiliary branches to improve summarization performance. Our method achieved top scores of 4.33 and 4.66 in the long caption and short caption tracks, respectively, of the 2024 SciCAP competition, ranking highest in both categories.
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GenerationPrograms: Fine-grained Attribution with Executable Programs
TextFlux: An OCR-Free DiT Model for High-Fidelity Multilingual Scene Text Synthesis
Hallucinate at the Last in Long Response Generation: A Case Study on Long Document Summarization
GlyphMastero: A Glyph Encoder for High-Fidelity Scene Text Editing
Making Language Model a Hierarchical Classifier and Generator
Mitigating Object Hallucinations via Sentence-Level Early Intervention
토론
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