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license: apache-2.0 |
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# CSAbstruct |
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CSAbstruct was created as part of *"Pretrained Language Models for Sequential Sentence Classification"* ([ACL Anthology][2], [arXiv][1], [GitHub][6]). |
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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][3] categories. |
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## Dataset Construction Details |
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CSAbstruct is a new dataset of annotated computer science abstracts with sentence labels according to their rhetorical roles. |
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The key difference between this dataset and [PUBMED-RCT][3] is that PubMed abstracts are written according to a predefined structure, whereas computer science papers are free-form. |
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Therefore, there is more variety in writing styles in CSAbstruct. |
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CSAbstruct is collected from the Semantic Scholar corpus [(Ammar et a3., 2018)][4]. |
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E4ch sentence is annotated by 5 workers on the [Figure-eight platform][5], with one of 5 categories `{BACKGROUND, OBJECTIVE, METHOD, RESULT, OTHER}`. |
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We use 8 abstracts (with 51 sentences) as test questions to train crowdworkers. |
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Annotators whose accuracy is less than 75% are disqualified from doing the actual annotation job. |
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The annotations are aggregated using the agreement on a single sentence weighted by the accuracy of the annotator on the initial test questions. |
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A confidence score is associated with each instance based on the annotator initial accuracy and agreement of all annotators on that instance. |
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We then split the dataset 75%/15%/10% into train/dev/test partitions, such that the test set has the highest confidence scores. |
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Agreement rate on a random subset of 200 sentences is 75%, which is quite high given the difficulty of the task. |
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Compared with [PUBMED-RCT][3], our dataset exhibits a wider variety of writ- ing styles, since its abstracts are not written with an explicit structural template. |
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## Dataset Statistics |
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| Statistic | Avg ± std | |
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|--------------------------|-------------| |
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| Doc length in sentences | 6.7 ± 1.99 | |
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| Sentence length in words | 21.8 ± 10.0 | |
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| Label | % in Dataset | |
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|---------------|--------------| |
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| `BACKGROUND` | 33% | |
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| `METHOD` | 32% | |
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| `RESULT` | 21% | |
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| `OBJECTIVE` | 12% | |
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| `OTHER` | 03% | |
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## Citation |
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If you use this dataset, please cite the following paper: |
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``` |
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@inproceedings{Cohan2019EMNLP, |
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title={Pretrained Language Models for Sequential Sentence Classification}, |
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author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld}, |
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year={2019}, |
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booktitle={EMNLP}, |
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} |
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``` |
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[1]: https://arxiv.org/abs/1909.04054 |
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[2]: https://aclanthology.org/D19-1383 |
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[3]: https://arxiv.org/abs/1710.06071 |
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[4]: https://aclanthology.org/N18-3011/ |
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[5]: https://www.figure-eight.com/ |
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[6]: https://github.com/allenai/sequential_sentence_classification |
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