---
license: apache-2.0
---


# CSAbstruct

CSAbstruct was created as part of ["Pretrained Language Models for Sequential Sentence Classification"][1].

It contains 2,189 manually annotated computer science abstracts with sentences annotated according to their rhetorical roles in the abstract, similar to the [PUBMED-RCT][2] categories.


## Dataset Construction Details

CSAbstruct is a new dataset of annotated computer science abstracts with sentence labels according to their rhetorical roles.
The key difference between this dataset and [PUBMED-RCT][2] is that PubMed abstracts are written according to a predefined structure, whereas computer science papers are free-form.
Therefore, there is more variety in writing styles in CSAbstruct.
CSAbstruct is collected from the Semantic Scholar corpus [(Ammar et al., 2018)][3].
Each sentence is annotated by 5 workers on the [Figure-eight platform][4], with one of 5 categories `{BACKGROUND, OBJECTIVE, METHOD, RESULT, OTHER}`.

We use 8 abstracts (with 51 sentences) as test questions to train crowdworkers.
Annotators whose accuracy is less than 75% are disqualified from doing the actual annotation job.
The annotations are aggregated using the agreement on a single sentence weighted by the accuracy of the annotator on the initial test questions.
A confidence score is associated with each instance based on the annotator initial accuracy and agreement of all annotators on that instance.
We then split the dataset 75%/15%/10% into train/dev/test partitions, such that the test set has the highest confidence scores.
Agreement rate on a random subset of 200 sentences is 75%, which is quite high given the difficulty of the task.
Compared with [PUBMED-RCT][2], our dataset exhibits a wider variety of writ- ing styles, since its abstracts are not written with an explicit structural template.

## Dataset Statistics

| Statistic                |  Avg ± std  |
|--------------------------|-------------|
| Doc length in sentences  |  6.7 ± 1.99 |
| Sentence length in words | 21.8 ± 10.0 |

| Label         | % in Dataset |
|---------------|--------------|
| `BACKGROUND`  | 33%          |
| `METHOD`      | 32%          |
| `RESULT`      | 21%          |
| `OBJECTIVE`   | 12%          |
| `OTHER`       | 03%          |

## Citation

If you use this dataset, please cite the following paper:

```
@inproceedings{Cohan2019EMNLP,
  title={Pretrained Language Models for Sequential Sentence Classification},
  author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld},
  year={2019},
  booktitle={EMNLP},
}
```

[1]: https://aclanthology.org/D19-1383
[2]: https://arxiv.org/abs/1710.06071
[3]: https://aclanthology.org/N18-3011/
[4]: https://www.figure-eight.com/