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README.md

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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 
-
-[![Watch the video](https://img.youtube.com/vi/C-fwW_ZhkE8/0.jpg)](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="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 |

multi_lexsum.py

@@ -1,155 +0,0 @@
-from typing import List, Union, Dict, Any, Tuple
-import json
-import os
-
-import datasets
-from datasets.tasks import Summarization
-
-logger = datasets.logging.get_logger(__name__)
-
-
-def _load_jsonl(filename):
-    with open(filename, "r") as fp:
-        jsonl_content = fp.read()
-
-    result = [json.loads(jline) for jline in jsonl_content.splitlines()]
-    return result
-
-
-def _load_json(filepath):
-
-    with open(filepath, "r") as fp:
-        res = json.load(fp)
-    return res
-
-
-_CITATION = """
-@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}
-}
-"""  # TODO
-
-_DESCRIPTION = """
-Multi-LexSum is a multi-doc summarization dataset for civil rights litigation lawsuits with summaries of three granularities. 
-"""  # TODO: Update with full abstract 
-
-_HOMEPAGE = "https://multilexsum.github.io"
-
-# _BASE_URL = "https://ai2-s2-research.s3.us-west-2.amazonaws.com/multilexsum/releases"
-_BASE_URL = "https://huggingface.co/datasets/allenai/multi_lexsum/resolve/main/releases"
-_FILES = {
-    "train": "train.json",
-    "dev": "dev.json",
-    "test": "test.json",
-    "sources": "sources.json",
-}
-
-
-class MultiLexsumConfig(datasets.BuilderConfig):
-    """BuilderConfig for LexSum."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for LexSum.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(MultiLexsumConfig, self).__init__(**kwargs)
-
-
-class MultiLexsum(datasets.GeneratorBasedBuilder):
-    """MultiLexSum Dataset: a multi-doc summarization dataset for 
-    civil rights litigation lawsuits with summaries of three granularities. 
-    """
-
-    BUILDER_CONFIGS = [
-        MultiLexsumConfig(
-            name="v20220616",
-            version=datasets.Version("1.0.0", "Public v1.0 release."),
-            description="The v1.0 Multi-LexSum dataset",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "sources": datasets.Sequence(datasets.Value("string")),
-                    "summary/long": datasets.Value("string"),
-                    "summary/short": datasets.Value("string"),
-                    "summary/tiny": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            task_templates=[
-                Summarization(text_column="source", summary_column="summary/long")
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-
-        base_url = _BASE_URL if self.config.data_dir is None else self.config.data_dir
-        downloaded_files = dl_manager.download_and_extract(
-            {
-                name: f"{base_url}/{self.config.name}/{filename}"
-                for name, filename in _FILES.items()
-            }
-        )
-        # Given sources is a large file, we read it first
-        sources = _load_json(downloaded_files["sources"])
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "subset_file": downloaded_files["train"],
-                    "sources": sources,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "subset_file": downloaded_files["dev"],
-                    "sources": sources,
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "subset_file": downloaded_files["test"],
-                    "sources": sources,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, subset_file: str, sources: Dict[str, Dict]):
-        """This function returns the examples in the raw (text) form."""
-        logger.info(f"generating examples from = {subset_file}")
-
-        subset_cases = _load_jsonl(subset_file)
-        for case_data in subset_cases:
-            case_sources = [
-                sources[source_id]["doc_text"]
-                for source_id in case_data["case_documents"]
-            ]
-            yield case_data["case_id"], {
-                "id": case_data["case_id"],
-                "sources": case_sources,
-                "summary/long": case_data["summary/long"],
-                "summary/short": case_data["summary/short"],
-                "summary/tiny": case_data["summary/tiny"],
-            }

releases/v20220616/train.json → v20220616/multi_lexsum-test.parquet

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