readme

README.md

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 license: apache-2.0
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+
+
+# 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/