README.md

---
language:
- en
license: cc-by-4.0
configs:
- config_name: default
  data_files:
  - split: train
    path: train/*
  - split: dev
    path: dev/*
  - split: test
    path: test/*
dataset_info:
  features:
  - name: video_path
    dtype: string
  - name: audio
    dtype: audio
  - name: sr
    dtype: int64
  - name: abstract
    dtype: string
  - name: language
    dtype: string
  - name: split
    dtype: string
  - name: duration
    dtype: float64
  - name: conference
    dtype: string
  - name: year
    dtype: string
  config_name: default
  splits:
  - name: train
    num_examples: 4000
  - name: dev
    num_examples: 885
  - name: test
    num_examples: 1431
tags:
- text
- audio
- video
---

# NUTSHELL: A Dataset for Abstract Generation from Scientific Talks

Scientific communication is receiving increasing attention in natural language processing, especially to help researches access, summarize, and generate content. 
One emerging application in this area is Speech-to-Abstract Generation (SAG), which aims to automatically generate abstracts from recorded scientific presentations. 
SAG enables researchers to efficiently engage with conference talks, but progress has been limited by a lack of large-scale datasets. 
To address this gap, we introduce NUTSHELL, a novel multimodal dataset of *ACL conference talks paired with their corresponding abstracts. 

More informatation can be found in our paper [NUTSHELL: A Dataset for Abstract Generation from Scientific Talks](https://arxiv.org/abs/2502.16942).
 

## Dataset Splits

| Split | Number of Examples |
|-------|--------------------|
| train | 4000               |
| dev   | 885                |
| test  | 1431               |


## Dataset Fields

| **Field**       | **Type**        | **Description**                                                                  |
|------------------|-----------------|---------------------------------------------------------------------------------|
| `video_path`     | `string`        | The video URL to the ACL talk.                                                  |
| `audio`          |                 |                                                                                 |
|                  | - `array`       | A `numpy.ndarray` representing the audio signal.                                |
|                  | - `sampling_rate` | The sampling rate of the audio.                                               |
| `sr`             | `int`           | The sampling rate of the audio.                                                 |
| `abstract`       | `string`        | The abstract of the ACL paper corresponding to the talk.                        |
| `language`       | `string`        | The language of the videos and audios: English.                                 |
| `split`          | `string`        | The data split to which the entry belongs, such as "train," "dev," or "test."   |
| `duration`       | `float`         | The duration of the video/audio content in seconds.                             |
| `conference`     | `string`        | The name of the conference associated with the dataset entry.                   |
| `year`           | `string`        | The year of the conference.                                                     |


## Citation
```
@inproceedings{zufle-etal-2025-nutshell,
    title = "{NUTSHELL}: A Dataset for Abstract Generation from Scientific Talks",
    author = {Z{\"u}fle, Maike  and
      Papi, Sara  and
      Savoldi, Beatrice  and
      Gaido, Marco  and
      Bentivogli, Luisa  and
      Niehues, Jan},
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.iwslt-1.2/",
    doi = "10.18653/v1/2025.iwslt-1.2",
    pages = "19--32",
    ISBN = "979-8-89176-272-5",
    abstract = "Scientific communication is receiving increasing attention in natural language processing, especially to help researches access, summarize, and generate content. One emerging application in this area is Speech-to-Abstract Generation (SAG), which aims to automatically generate abstracts from recorded scientific presentations. SAG enables researchers to efficiently engage with conference talks, but progress has been limited by a lack of large-scale datasets. To address this gap, we introduce NUTSHELL, a novel multimodal dataset of *ACL conference talks paired with their corresponding abstracts. We establish strong baselines for SAG and evaluate the quality of generated abstracts using both automatic metrics and human judgments. Our results highlight the challenges of SAG and demonstrate the benefits of training on NUTSHELL. By releasing NUTSHELL under an open license (CC-BY 4.0), we aim to advance research in SAG and foster the development of improved models and evaluation methods."
}
```