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.
@article{arxiv.2403.16941,
title = {SPACE-IDEAS: A Dataset for Salient Information Detection in Space Innovation},
author = {Andrés García-Silva and Cristian Berrío and José Manuel Gómez-Pérez},
journal= {arXiv preprint arXiv:2403.16941},
year = {2024}
}