Proposal Report for the 2nd SciCAP Competition 2024
This is an incremental improvement for summarizing scientific documents with images and tables.
The paper tackled document summarization by using auxiliary information like OCR data and extracted text to enhance text generation models, achieving top scores of 4.33 and 4.66 in the 2024 SciCAP competition tracks.
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.