# Proposal Report for the 2nd SciCAP Competition 2024

arXiv: https://arxiv.org/abs/2407.01897 · HTML: https://mlsanity.com/paper/2407.01897/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 17 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Image Segmentation Using Text and Image Prompts](https://mlsanity.com/paper/2112.10003/) (2021)
- [Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport](https://mlsanity.com/paper/2104.08489/) (2021)
- [Exploiting Cross-Modal Prediction and Relation Consistency for Semi-Supervised Image Captioning](https://mlsanity.com/paper/2110.11767/) (2021)
- [Learning Adaptive Embedding Considering Incremental Class](https://mlsanity.com/paper/2008.13351/) (2020)
- [Robust Semi-supervised Learning by Wisely Leveraging Open-set Data](https://mlsanity.com/paper/2405.06979/) (2024)
- [MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes](https://mlsanity.com/paper/2404.09232/) (2024)

## Built on by (most cited first, 2 of 2)

- [Multi-LLM Collaborative Caption Generation in Scientific Documents](https://mlsanity.com/paper/2501.02552/) (2025)
- [Leveraging Author-Specific Context for Scientific Figure Caption Generation: 3rd SciCap Challenge](https://mlsanity.com/paper/2510.07993/) (2025)

## 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.