---
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",
}
```