Update README.md

README.md

@@ -34,52 +34,43 @@ language:
 - en
 tags:
 - legal-nlp
+- case-retrieval
+- legal-summarization
+- argument-completion
 size_categories:
 - 10K<n<100K
 ---
-# Dataset Card for Dataset Name
+# Dataset Card for BriefMe
 
 <!-- Provide a quick summary of the dataset. -->
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
-
 ## Dataset Details
 
 ### Dataset Description
 
 <!-- Provide a longer summary of what this dataset is. -->
 
+Legal work involves reading, writing and reasoning about large volumes of text. For instance, in litigation, attorneys must search and interpret prior case law to construct convincing arguments to stake out a strong position. They present these to a judge in the form of a document called a brief. 
 
+BriefMe is a legal NLP corpus that presents three tasks on which to train and evaluate language models to assist in legal brief writing and argumentation: 
 
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+    (1) Argument Summarization: given a section of text from a brief, summarize the argument as a section heading for that part of the brief
+    (2) Argument Completion: given a set of arguments comprised of the section headings in a brief, complete the missing argument
+    (3) Case Retrieval: given a context window of text from the brief, retrieve the judicial opinion that supports the argument in the brief
 
-## Uses
+Argument completion is a novel task that leverages the inherent structure of a brief, where briefs are split into multiple sections that constitute sub-arguments about the state of the law as interpreted by that attorney, and effective arguments fit together into a cohesive whole that logically walks the judge step-by-step to the attorney's preferred outcome.
 
-<!-- Address questions around how the dataset is intended to be used. -->
+BriefMe is built using legal briefs filed in cases argued in the Supreme Court of the United States (SCOTUS).
 
-### Direct Use
+- **Language(s) (NLP):** English
+- **License:** CC-BY
 
-<!-- This section describes suitable use cases for the dataset. -->
+### Dataset Sources
 
-[More Information Needed]
-
-### Out-of-Scope Use
+<!-- Provide the basic links for the dataset. -->
 
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-
-[More Information Needed]
+- **Repository:** https://github.com/utahnlp/BriefMe
+- **Paper:** BriefMe: A Legal NLP Benchmark for Assisting with Legal Briefs
 
 ## Dataset Structure
 
@@ -97,41 +88,36 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 ### Source Data
 
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+Briefs were scraped from www.supremecourt.gov. The judicial opinion retrieval corpus is compiled using the Court Listener API.
 
 #### Data Collection and Processing
 
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
 
-[More Information Needed]
+Review the related paper for details on data collection and processing.
 
 #### Who are the source data producers?
 
 <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
 
-[More Information Needed]
+Briefs were produced by attorneys arguing cases in front of the Supreme Court. Judicial opinions are produced by U.S. judges.
 
 ### Annotations [optional]
 
 <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
 
+
 #### Annotation process
 
 <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
 
-[More Information Needed]
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-[More Information Needed]
+Headings for the argument completion and summarization tasks include a rating from an LLM judge that was calibrated against human annotators with legal training retained through Qualtrics. These ratings can be used to filter the data for a higher quality set. See the related paper for additional details. 
 
 #### Personal and Sensitive Information
 
 <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
 
-[More Information Needed]
+The data may contain personal information, although all data is from publicly filed court documents or judicial opinions, so it may not be considered "private."
 
 ## Bias, Risks, and Limitations
 
@@ -139,13 +125,7 @@ This dataset card aims to be a base template for new datasets. It has been gener
 
 [More Information Needed]
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-
-## Citation [optional]
+## Citation
 
 <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
 
@@ -155,22 +135,8 @@ Users should be made aware of the risks, biases and limitations of the dataset.
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Dataset Card Authors [optional]
-
-[More Information Needed]
+Jesse Woo, Fateme Hashemi Chaleshtori, Ana Marasović, and Kenneth Marino. 2025. BriefMe: A Legal NLP Benchmark for Assisting with Legal Briefs. In Findings of the Association for Computational Linguistics: ACL 2025, Vienna, Austria. Association for Computational Linguistics.
 
 ## Dataset Card Contact
 
-[More Information Needed]
+Jesse Woo: jw4202@columbia.edu