Proposal Report for the 2nd SciCAP Competition 2024
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866916309166456832 |
|---|---|
| author | Li, Pengpeng Li, Tingmin Wang, Jingyuan Wang, Boyuan Yang, Yang |
| author_facet | Li, Pengpeng Li, Tingmin Wang, Jingyuan Wang, Boyuan Yang, Yang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01897 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Proposal Report for the 2nd SciCAP Competition 2024 Li, Pengpeng Li, Tingmin Wang, Jingyuan Wang, Boyuan Yang, Yang Computation and Language 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. |
| title | Proposal Report for the 2nd SciCAP Competition 2024 |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2407.01897 |