Search Results for author:
Found 7 papers, 2 papers with code
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
Crafting effective captions for figures is important.
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization
Prior work often treated figure caption generation as a vision-to-language task.
Summarizing Community-based Question-Answer Pairs
To help users quickly digest the key information, we propose the novel CQA summarization task that aims to create a concise summary from CQA pairs.
SciCap: Generating Captions for Scientific Figures
Researchers use figures to communicate rich, complex information in scientific papers.
Ranked #1 on
Image Captioning
on SCICAP
Visual Story Post-Editing
We introduce the first dataset for human edits of machine-generated visual stories and explore how these collected edits may be used for the visual story post-editing task.
On How Users Edit Computer-Generated Visual Stories
A significant body of research in Artificial Intelligence (AI) has focused on generating stories automatically, either based on prior story plots or input images.
