Datasets:
Modalities:
Text
Formats:
csv
Sub-tasks:
semantic-similarity-scoring
Languages:
English
Size:
10M - 100M
ArXiv:
License:
| 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 `wiki-paragraphs` | |
| ## 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](aumiller@informatik.uni-heidelberg.de) | |
| ### 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} | |
| } | |
| ``` |