updated readme

README.md

@@ -5,18 +5,18 @@ license: apache-2.0
 
 # CSAbstruct
 
-CSAbstruct was created as part of ["Pretrained Language Models for Sequential Sentence Classification"][1].
+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][2] categories.
+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][2] is that PubMed abstracts are written according to a predefined structure, whereas computer science papers are free-form.
+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 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}`.
+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.
@@ -24,7 +24,7 @@ The annotations are aggregated using the agreement on a single sentence weighted
 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.
+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
 
@@ -54,7 +54,9 @@ If you use this dataset, please cite the following paper:
 }
 ```
 
-[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/
+[1]: https://arxiv.org/abs/1909.04054
+[2]: https://aclanthology.org/D19-1383
+[3]: https://arxiv.org/abs/1710.06071
+[4]: https://aclanthology.org/N18-3011/
+[5]: https://www.figure-eight.com/
+[6]: https://github.com/allenai/sequential_sentence_classification