---
dataset_info:
  features:
  - name: doc_id
    dtype: string
  - name: sentences
    list: string
  - name: labels
    list: string
  - name: title
    dtype: string
  splits:
  - name: train
    num_bytes: 202808
    num_examples: 112
  - name: validation
    num_bytes: 55840
    num_examples: 28
  - name: test
    num_bytes: 67098
    num_examples: 36
  download_size: 331303
  dataset_size: 325746
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
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.",
   }
```