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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."
}
``` |