Proposal Report for the 2nd SciCAP Competition 2024
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 10 of its 17 references that are in the map. First posted on arXiv 2 Jul 2024.
Builds on
10 of 17 references.
References · 10
- 2017Attention Is All You Needcited by 59,932
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,100
- 2021Image Segmentation Using Text and Image Promptscited by 509
- 2021Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transportcited by 28
- 2021Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioningcited by 23
- 2020Learning Adaptive Embedding Considering Incremental Classcited by 21
- 2024Robust Semi-supervised Learning by Wisely Leveraging Open-set Datacited by 20
- 2024MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classescited by 7
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Citing papers per year: 2025: 2.
2 papers.