expertailab/SPACE-IDEAS

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@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 c

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dataset_licence({ "id": "expertailab/SPACE-IDEAS" })
signal: "permissive"
stated_licence: "cc-by-4.0"
sources: 0