Papers by Ting-Yao Hsu
Summarizing Community-based Question-Answer Pairs (2022.emnlp-main)
Copied to clipboard
| Challenge: | Community-based question answering (CQA) has become an essential component of online services. |
| Approach: | They propose a novel task to summarize CQA pairs into a concise summary . they use a benchmark dataset and a sentence-type transfer and deduplication removal approach . |
| Outcome: | The proposed task aims to create a concise summary from CQA pairs . the proposed method is stronger than existing methods and is publicly available . |
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing algorithms that generate captions for scientific figures are costly and dependent on author-written captions. |
| Approach: | They constructed a human evaluation dataset that contains human judgments for 3,600 scientific figure captions for 600 arXiv figures. |
| Outcome: | The proposed model outperforms all other models and outperformed undergraduates in achieving a Kendall correlation score of 0.401 with Ph.D. students’ rankings. |
SciCap: Generating Captions for Scientific Figures (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Low-quality captions are common in scientific articles and can decrease understanding . this paper aims to develop an end-to-end neural framework to generate informative, high-quality figure captions for scientific figures and charts. |
| Approach: | They propose an end-to-end neural framework to automatically generate captions for scientific figures from a large-scale dataset . they used figure-type classification, sub-figure identification, text normalization, and caption text selection to build models that caption graph plots, the dominant figure type. |
| Outcome: | The proposed model can generate high-quality captions for scientific figures and charts from a large figure-caption dataset from arXiv. |
Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023 (2026.tacl-1)
Copied to clipboard
Ting-Yao Hsu, Yi-Li Hsu, Shaurya Rohatgi, Chieh-Yang Huang, Ho Yin Sam Ng, Ryan Rossi, Sungchul Kim, Tong Yu, Lun-Wei Ku, Clyde Lee Giles, Ting-Hao Huang
| Challenge: | SciCap dataset launched in 2021 aims to generate high-quality captions for scientific figures. |
| Approach: | They propose to use the SciCap dataset to develop models for captioning diverse figure types across various academic fields. |
| Outcome: | The proposed models showed impressive performance on the SciCap dataset and in various vision-and-language tasks. |
Visual Story Post-Editing (P19-1)
Copied to clipboard
| Challenge: | a dataset for human edits of machine-generated visual stories is released . it includes 14,905 human-edited versions of 2,981 machine- generated visual stories . |
| Approach: | They introduce the first dataset for human edits of machine-generated visual stories . they explore how edits may be used for the visual story post-editing task . |
| Outcome: | The proposed dataset includes 14,905 human-edited versions of 2,981 machine-generated visual stories. |