Datasets:

dongqi-me
/

VISTA

license: cc-by-4.0
task_categories:
  - summarization
  - text-generation
size_categories:
  - n>1T
language:
  - en
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VISTA Dataset

Dataset Structure

dataset/
├── videos/                 # Video files directory
│   ├── train_part1/        # Training set videos (first 8000 samples)
│   ├── train_part2/        # Training set videos (remaining samples)
│   ├── val/                # Validation set videos
│   └── test/               # Test set videos
├── train_part1.json        # Training set metadata (first 8000 samples)
├── train_part2.json        # Training set metadata (remaining samples)
├── val.json                # Validation set metadata
├── test.json               # Test set metadata
└── dataset_info.json       # Dataset description and statistics

Note: Due to the limitation of Hugging Face datasets where a single file folder can store up to 10000 files, the training set videos have been split into train_part1 and train_part2 folders.

Video Files

The video files are placed in the videos/ folder. Videos are organized by split (train_part1/train_part2/val/test).

Metadata Format

Each sample includes the following information:

id: Sample ID
title: Paper title
authors: List of authors
abstract: Paper abstract (ground truth summary)
video_file: Video filename
video_path: Path to the video file (this is processed as a video feature)
paper_url: Link to the paper PDF
venue: Publication venue

Usage with Hugging Face Datasets

from datasets import load_dataset

# Load the dataset from Hugging Face Hub
dataset = load_dataset("dongqi-me/VISTA")

# Access splits
train_part1 = dataset["train_part1"]
train_part2 = dataset["train_part2"]
val_data = dataset["validation"]
test_data = dataset["test"]

# Access videos
video_part1 = train_part1[0]["video_path"]  # Access video in train_part1
video_part2 = train_part2[0]["video_path"]  # Access video in train_part2

# Or load from local directory
local_dataset = load_dataset("json", data_files={
    "train_part1": "train_part1.json",
    "train_part2": "train_part2.json",
    "validation": "val.json",
    "test": "test.json"
})

Citation

If you use this dataset in your research, please cite:

@inproceedings{liu-etal-2025-talk,
    title = "What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations",
    author = "Liu, Dongqi  and
      Whitehouse, Chenxi  and
      Yu, Xi  and
      Mahon, Louis  and
      Saxena, Rohit  and
      Zhao, Zheng  and
      Qiu, Yifu  and
      Lapata, Mirella  and
      Demberg, Vera",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-long.310/",
    pages = "6187--6210",
    ISBN = "979-8-89176-251-0",
    abstract = "Transforming recorded videos into concise and accurate textual summaries is a growing challenge in multimodal learning. This paper introduces VISTA, a dataset specifically designed for video-to-text summarization in scientific domains. VISTA contains 18,599 recorded AI conference presentations paired with their corresponding paper abstracts. We benchmark the performance of state-of-the-art large models and apply a plan-based framework to better capture the structured nature of abstracts. Both human and automated evaluations confirm that explicit planning enhances summary quality and factual consistency. However, a considerable gap remains between models and human performance, highlighting the challenges of our dataset. This study aims to pave the way for future research on scientific video-to-text summarization."
}