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---
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
- **Homepage:** https://multilexsum.github.io
- **Repository:** https://github.com/multilexsum/dataset
- **Paper:** https://arxiv.org/abs/2206.10883
<p>
<a href="https://multilexsum.github.io" style="display: inline-block;">
<img src="https://img.shields.io/badge/-homepage-informational.svg?logo=jekyll" title="Multi-LexSum Paper" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a>
<a href="https://github.com/multilexsum/dataset" style="display: inline-block;">
<img src="https://img.shields.io/badge/-multilexsum-lightgrey.svg?logo=github" title="Multi-LexSum Github Repo" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a>
<a href="https://arxiv.org/abs/2206.10883" style="display: inline-block;">
<img src="https://img.shields.io/badge/NeurIPS-2022-9cf" title="Multi-LexSum is accepted in NeurIPS 2022" style="margin-top: 0.25rem; margin-bottom: 0.25rem"></a>
</p>
### Talk @ NeurIPS 2022
[](https://youtu.be/C-fwW_ZhkE8)
### 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="v20230518")
# 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
print(example['case_metadata']) # The corresponding metadata for a case in a dict
```
### 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 |
| ----------: | -----------------------------------------------------------: |
| `v20230518` | The v1.1 release including case and source document metadata |
| `v20220616` | The initial v1.0 release | |