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| license: apache-2.0 | |
| # CSAbstruct | |
| CSAbstruct was created as part of *"Pretrained Language Models for Sequential Sentence Classification"* ([ACL Anthology][2], [arXiv][1], [GitHub][6]). | |
| 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. | |
| ## 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][3] 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 a3., 2018)][4]. | |
| E4ch sentence is annotated by 5 workers on the [Figure-eight platform][5], 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][3], 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://arxiv.org/abs/1909.04054 | |
| [2]: https://aclanthology.org/D19-1383 | |
| [3]: https://github.com/Franck-Dernoncourt/pubmed-rct | |
| [4]: https://aclanthology.org/N18-3011/ | |
| [5]: https://www.figure-eight.com/ | |
| [6]: https://github.com/allenai/sequential_sentence_classification | |