Update README.md

README.md

@@ -37,3 +37,41 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+# POSTERSUM Dataset
+
+## Dataset Summary
+
+The **POSTERSUM** dataset is a multimodal benchmark designed for the summarization of scientific posters into research paper abstracts. The dataset consists of **16,305** research posters collected from major machine learning conferences, including ICLR, ICML, and NeurIPS, spanning the years **2022-2024**. Each poster is provided in image format along with its corresponding abstract as a summary. This dataset is intended for research in multimodal understanding and summarization tasks, particularly in vision-language models (VLMs) and Multimodal Large Language Models (MLLMs).
+
+## Dataset Details
+
+### Data Fields
+Each record in the dataset contains the following fields:
+- `conference` (*string*): Name of the conference where the research poster was presented (e.g., ICLR, ICML, NeurIPS).
+- `year` (*int*): The year of the conference.
+- `paper_id` (*int*): Conference identifier for the research paper associated with the poster.
+- `title` (*string*): The title of the research paper.
+- `abstract` (*string*): The human-written abstract of the paper, serving as the ground-truth summary for the poster.
+- `topics` (*list of strings*): Machine learning topics related to the research (e.g., Reinforcement Learning, Natural Language Processing, Graph Neural Networks).
+- `image_url` (*string*): URL to the image file of the scientific poster.
+
+### Dataset Statistics
+- **Total number of poster-summary pairs:** 16,305
+- **Total number of unique topics:** 137
+- **Average summary length:** 224 tokens
+- **Train/Validation/Test split:** 10,305 / 3,000 / 3,000
+
+
+## Citation
+```
+@misc{saxena2025postersummultimodalbenchmarkscientific,
+      title={PosterSum: A Multimodal Benchmark for Scientific Poster Summarization}, 
+      author={Rohit Saxena and Pasquale Minervini and Frank Keller},
+      year={2025},
+      eprint={2502.17540},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2502.17540}, 
+}
+```