| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - en |
| tags: |
| - tasksource |
| - extraction |
| configs: |
| - config_name: neoplasm |
| data_files: |
| - split: train |
| path: train.parquet |
| - split: validation |
| path: validation.parquet |
| - split: test |
| path: test.parquet |
| - config_name: glaucoma |
| data_files: |
| - split: test |
| path: glaucoma/test.parquet |
| - config_name: mixed |
| data_files: |
| - split: test |
| path: mixed/test.parquet |
| --- |
| # AbstRCT argument-component spans |
|
|
| Major claim, claim and premise argument-component spans in MEDLINE randomized-controlled-trial (RCT) abstracts |
| (Mayer, Cabrio and Villata, ECAI 2020), expert-annotated with the brat tool (two annotators, adjudicated). Every |
| context is the full abstract, not an isolated sentence: whether a sentence is a claim or a premise is judged |
| against the rest of the abstract, which a per-sentence view would strip away. |
|
|
| Data-only mirror of the [original brat distribution](https://gitlab.com/tomaye/abstrct) (revision |
| `f856f1ca7514caa4094194b5623e84c159d9bf1d`, archive SHA256 `32150598d2e9`). Reproduce with |
| [scripts/repackage_dataset/abstrct_spans.py](https://github.com/sileod/tasksource/blob/main/scripts/repackage_dataset/abstrct_spans.py); |
| [provenance.json](provenance.json) records the archive hash, revision, native abstract counts and settings. |
| License: CC BY-NC-SA 4.0 ([evidence](https://gitlab.com/tomaye/abstrct/-/blob/f856f1ca7514caa4094194b5623e84c159d9bf1d/LICENSE)). |
|
|
| Schema: `context` (the abstract), `question`, `answers` (`text`, `answer_start`: every span, not alternatives) and |
| JSON-string `metadata` (`field` is one of `major_claim`, `claim`, `premise`; `group` is `{config}:{PMID}`). |
| Every field is always asked; an abstract with none of a kind answers with null. Used by the `extraction` config of |
| [tasksource-jev-typed-decisions](https://huggingface.co/datasets/tasksource/tasksource-jev-typed-decisions) |
| (`neoplasm` config, as `abstrct/neoplasm`). |
|
|
| `neoplasm` has the native train/dev/test split used for training. `glaucoma` and `mixed` are test-only |
| cross-domain transfer sets (no training abstracts) and are evaluation-only. |
|
|
| ## Citation |
|
|
| Please cite the original dataset: |
|
|
| ```bibtex |
| @inproceedings{mayer2020ecai, |
| author = {Tobias Mayer and Elena Cabrio and Serena Villata}, |
| title = {Transformer-Based Argument Mining for Healthcare Applications}, |
| booktitle = {{ECAI} 2020 - 24th European Conference on Artificial Intelligence}, |
| series = {Frontiers in Artificial Intelligence and Applications}, |
| volume = {325}, |
| pages = {2108--2115}, |
| publisher = {{IOS} Press}, |
| year = {2020}, |
| } |
| ``` |
|
|
| ## Citing tasksource |
|
|
| If you use tasksource, its standardized task annotations, or datasets derived from them in published research, please cite the tasksource paper (Sileo, LREC-COLING 2024). |
|
|
| ```bibtex |
| @inproceedings{sileo-2024-tasksource, |
| title = "tasksource: A Large Collection of {NLP} tasks with a Structured Dataset Preprocessing Framework", |
| author = "Sileo, Damien", |
| booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)", |
| month = may, |
| year = "2024", |
| address = "Torino, Italia", |
| publisher = "ELRA and ICCL", |
| url = "https://aclanthology.org/2024.lrec-main.1361", |
| doi = "10.63317/2h4pkr685mkq", |
| pages = "15655--15684", |
| } |
| ``` |
|
|
