Add dataset card.

README.md

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----
-license: cc-by-sa-3.0
----
+annotations_creators:
+- machine-generated
+language:
+- en
+language_creators:
+- crowdsourced
+license:
+- cc-by-sa-3.0
+multilinguality:
+- monolingual
+pretty_name: wiki-paragraphs
+size_categories:
+- 10M<n<100M
+source_datasets:
+- original
+tags:
+- wikipedia
+- self-similarity
+task_categories:
+- text-classification
+- sentence-similarity
+task_ids:
+- semantic-similarity-scoring
+
+# Dataset Card for [Needs More Information]
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** [Needs More Information]
+- **Repository:** https://github.com/dennlinger/TopicalChange
+- **Paper:** https://arxiv.org/abs/2012.03619
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [Dennis Aumiller]([email protected])
+
+### Dataset Summary
+
+The wiki-paragraphs dataset is constructed by automatically sampling two paragraphs from a Wikipedia article. If they are from the same section, they will be considered a "semantic match", otherwise as "dissimilar". Dissimilar paragraphs can in theory also be sampled from other documents, but have not shown any improvement in the particular evaluation of the linked work.  
+The alignment is in no way meant as an accurate depiction of similarity, but allows to quickly mine large amounts of samples.
+
+### Supported Tasks and Leaderboards
+
+The dataset can be used for "same-section classification", which is a binary classification task (either two sentences/paragraphs belong to the same section or not).
+This can be combined with document-level coherency measures, where we can check how many misclassifications appear within a single document.
+Please refer to [our paper](https://arxiv.org/abs/2012.03619) for more details.
+
+### Languages
+
+The data was extracted from English Wikipedia, therefore predominantly in English.
+
+## Dataset Structure
+
+### Data Instances
+
+A single instance contains three attributes:
+
+```
+{
+  "sentence1": "<Sentence from the first paragraph>",
+  "sentence2": "<Sentence from the second paragraph>",
+  "label": 0/1 # 1 indicates two belong to the same section
+}
+```
+
+### Data Fields
+
+- sentence1: String containing the first paragraph
+- sentence2: String containing the second paragraph
+- label: Integer, either 0 or 1. Indicates whether two paragraphs belong to the same section (1) or come from different sections (0)
+
+### Data Splits
+
+We provide train, validation and test splits, which were split as 80/10/10 from a randomly shuffled original data source.
+In total, we provide 25375583 training pairs, as well as 3163685 validation and test instances, respectively.
+
+## Dataset Creation
+
+### Curation Rationale
+
+The original idea was applied to self-segmentation of Terms of Service documents. Given that these are of domain-specific nature, we wanted to provide a more generally applicable model trained on Wikipedia data.  
+It is meant as a cheap-to-acquire pre-training strategy for large-scale experimentation with semantic similarity for long texts (paragraph-level).
+Based on our experiments, it is not necessarily sufficient by itself to replace traditional hand-labeled semantic similarity datasets.
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+The data was collected based on the articles considered in the Wiki-727k dataset by Koshorek et al. The dump of their dataset can be found through the [respective Github repository](https://github.com/koomri/text-segmentation). Note that we did *not* use the pre-processed data, but rather only information on the considered articles, which were re-acquired from Wikipedia at a more recent state.
+This is due to the fact that paragraph information was not retained by the original Wiki-727k authors.
+We did not verify the particular focus of considered pages.
+
+#### Who are the source language producers?
+
+We do not have any further information on the contributors; these are volunteers contributing to en.wikipedia.org.
+
+### Annotations
+
+#### Annotation process
+
+No manual annotation was added to the dataset.
+We automatically sampled two sections from within the same article; if these belong to the same section, they were assigned a label indicating the "similarity" (1), otherwise the label indicates that they are not belonging to the same section (0).
+We sample three positive and three negative samples per section, per article.
+
+#### Who are the annotators?
+
+No annotators were involved in the process.
+
+### Personal and Sensitive Information
+
+We did not modify the original Wikipedia text in any way. Given that personal information, such as dates of birth (e.g., for a person of interest) may be on Wikipedia, this information is also considered in our dataset.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The purpose of the dataset is to serve as a *pre-training addition* for semantic similarity learning.
+
+Systems building on this dataset should consider additional, manually annotated data, before using a system in production.
+
+### Discussion of Biases
+
+To our knowledge, there are some works indicating that male people have a several times larger chance of having a Wikipedia page created (especially in historical contexts). Therefore, a slight bias towards over-representation might be left in this dataset.
+
+### Other Known Limitations
+
+As previously stated, the automatically extracted semantic similarity is not perfect; it should be treated as such.
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset was originally developed as a practical project by Lucienne-Sophie Marm� under the supervision of Dennis Aumiller.
+Contributions to the original sampling strategy were made by Satya Almasian and Michael Gertz
+
+### Licensing Information
+
+Wikipedia data is available under the CC-BY-SA 3.0 license.
+
+### Citation Information
+
+@inproceedings{DBLP:conf/icail/AumillerAL021,
+  author    = {Dennis Aumiller and
+               Satya Almasian and
+               Sebastian Lackner and
+               Michael Gertz},
+  editor    = {Juliano Maranh{\~{a}}o and
+               Adam Zachary Wyner},
+  title     = {Structural text segmentation of legal documents},
+  booktitle = {{ICAIL} '21: Eighteenth International Conference for Artificial Intelligence
+               and Law, S{\~{a}}o Paulo Brazil, June 21 - 25, 2021},
+  pages     = {2--11},
+  publisher = {{ACM}},
+  year      = {2021},
+  url       = {https://doi.org/10.1145/3462757.3466085},
+  doi       = {10.1145/3462757.3466085}
+}