license: apache-2.0
CSAbstruct
CSAbstruct was created as part of "Pretrained Language Models for Sequential Sentence Classification".
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 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 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).
Each sentence is annotated by 5 workers on the Figure-eight platform, 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, 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},
}