Update README

README.md

@@ -1,3 +1,135 @@
 ---
-license: odc-by
+annotations_creators:
+- expert-generated
+language:
+- en
+language_creators:
+- found
+license:
+- odc-by
+multilinguality:
+- monolingual
+pretty_name: Multi-LexSum
+size_categories:
+- 1K<n<10K
+- 10K<n<100K
+source_datasets:
+- original
+tags: []
+task_categories:
+- summarization
+task_ids: []
 ---
+
+# Dataset Card for Multi-LexSum
+## Table of Contents
+- [Dataset Card for Multi-LexSum](#dataset-card-for-multi-lexsum)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Languages](#languages)
+  - [Dataset](#dataset)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Sheet (Datasheet)](#dataset-sheet-datasheet)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+  - [Release History](#release-history)
+
+## Dataset Description
+
+| <a href="https://multilexsum.github.io"><img src="https://img.shields.io/badge/-homepage-informational.svg?logo=jekyll" title="Multi-LexSum Paper"></a>  | <a href="https://github.com/multilexsum/dataset"><img src="https://img.shields.io/badge/-multilexsum-lightgrey.svg?logo=github" title="Multi-LexSum Github Repo"></a> | <a href="https://arxiv.org/abs/2206.10883"><img src="https://img.shields.io/badge/arXiv-2206.10883-b31b1b.svg" title="Multi-LexSum Paper"></a>
+|-- | -- | -- | 
+
+
+### Dataset Summary
+
+The Multi-LexSum dataset is a collection of 9,280 such legal case summaries. Multi-LexSum is distinct from other datasets in its **multiple target summaries, each at a different granularity** (ranging from one-sentence “extreme” summaries to multi-paragraph narrations of over five hundred words). It presents a challenging multi-document summarization task given **the long length of the source documents**, often exceeding two hundred pages per case. Unlike other summarization datasets that are (semi-)automatically curated, Multi-LexSum consists of **expert-authored summaries**: the experts—lawyers and law students—are trained to follow carefully created guidelines, and their work is reviewed by an additional expert to ensure quality.
+
+### Languages
+
+English
+
+## Dataset 
+
+### Data Fields
+
+The dataset contains a list of instances (cases); each instance contains the following data: 
+
+|         Field |                                                                      Description |
+| ------------: | -------------------------------------------------------------------------------: |
+|            id |                                                              `(str)` The case ID |
+|       sources | `(List[str])` A list of strings for the text extracted from the source documents |
+|  summary/long |                         `(str)` The long (multi-paragraph) summary for this case |
+| summary/short |                `(Optional[str])` The short (one-paragraph) summary for this case |
+|  summary/tiny |                  `(Optional[str])` The tiny (one-sentence) summary for this case |
+
+Please check the exemplar usage below for loading the data: 
+
+```python
+from datasets import load_dataset
+
+multi_lexsum = load_dataset("allenai/multi_lexsum", name="v20220616")
+# Download multi_lexsum locally and load it as a Dataset object 
+
+example = multi_lexsum["validation"][0] # The first instance of the dev set 
+example["sources"] # A list of source document text for the case
+
+for sum_len in ["long", "short", "tiny"]:
+    print(example["summary/" + sum_len]) # Summaries of three lengths
+```
+
+### Data Splits
+
+|             | Instances | Source Documents (D) | Long Summaries (L) | Short Summaries (S) | Tiny Summaries (T) | Total Summaries |
+| ----------: | --------: | -------------------: | -----------------: | ------------------: | -----------------: | --------------: |
+| Train (70%) |     3,177 |               28,557 |              3,177 |               2,210 |              1,130 |           6,517 |
+|  Test (20%) |       908 |                7,428 |                908 |                 616 |                312 |           1,836 |
+|   Dev (10%) |       454 |                4,134 |                454 |                 312 |                161 |             927 |
+
+
+## Dataset Sheet (Datasheet)
+
+Please check our [dataset sheet](https://multilexsum.github.io/datasheet) for details regarding dataset creation, source data, annotation, and considerations for the usage. 
+
+## Additional Information
+
+### Dataset Curators
+
+The dataset is created by the collaboration between Civil Rights Litigation Clearinghouse (CRLC, from University of Michigan) and Allen Institute for AI. Multi-LexSum builds on the dataset used and posted by the Clearinghouse to inform the public about civil rights litigation. 
+
+### Licensing Information
+
+The Multi-LexSum dataset is distributed under the [Open Data Commons Attribution License (ODC-By)](https://opendatacommons.org/licenses/by/1-0/). 
+The case summaries and metadata are licensed under the [Creative Commons Attribution License (CC BY-NC)](https://creativecommons.org/licenses/by-nc/4.0/), and the source documents are already in the public domain. 
+Commercial users who desire a license for summaries and metadata can contact [info@clearinghouse.net](mailto:info@clearinghouse.net), which will allow free use but limit summary re-posting. 
+The corresponding code for downloading and loading the dataset is licensed under the Apache License 2.0. 
+
+### Citation Information
+
+
+```
+@article{Shen2022MultiLexSum,
+  author    = {Zejiang Shen and
+               Kyle Lo and
+               Lauren Yu and
+               Nathan Dahlberg and
+               Margo Schlanger and
+               Doug Downey},
+  title     = {Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities},
+  journal   = {CoRR},
+  volume    = {abs/2206.10883},
+  year      = {2022},
+  url       = {https://doi.org/10.48550/arXiv.2206.10883},
+  doi       = {10.48550/arXiv.2206.10883}
+}
+```
+
+
+## Release History 
+
+|     Version |              Description |
+| ----------: | -----------------------: |
+| `v20220616` | The initial v1.0 release |