SPACE-IDEAS: A Dataset for Salient Information Detection in Space
  Innovation
Andrés García-Silva Cristian Berrío José Manuel Gómez-Pérez
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.