metadata
dataset_info:
features:
- name: doc_id
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: annotation
dtype: string
- name: sentences
list: string
- name: labels
list: string
splits:
- name: train
num_bytes: 4983111
num_examples: 1020
download_size: 3558764
dataset_size: 4983111
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
license: cc-by-4.0
How to cite
To cite this research please use the following:
@inproceedings{garcia-silva-etal-2024-space-ideas,
title = "{SPACE}-{IDEAS}: A Dataset for Salient Information Detection in Space Innovation",
author = "Garcia-Silva, Andres and
Berrio, Cristian and
Gomez-Perez, Jose Manuel",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italy",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1311",
pages = "15087--15092",
abstract = "Detecting salient parts in text using natural language processing has been widely used to mitigate the effects of information overflow. Nevertheless, most of the datasets available for this task are derived mainly from academic publications. We introduce SPACE-IDEAS, a dataset for salient information detection from innovation ideas related to the Space domain. The text in SPACE-IDEAS varies greatly and includes informal, technical, academic and business-oriented writing styles. In addition to a manually annotated dataset we release an extended version that is annotated using a large generative language model. We train different sentence and sequential sentence classifiers, and show that the automatically annotated dataset can be leveraged using multitask learning to train better classifiers.",
}