Multi-LexSum: Real-world Summaries of Civil Rights Lawsuits at Multiple GranularitiesDownload PDF

06 Jun 2022, 04:31 (modified: 12 Oct 2022, 05:17)NeurIPS 2022 Datasets and Benchmarks Readers: Everyone
Keywords: Abstractive Summarization, Multi-Document Summarization, Legal Document Summarization, Controlled Summarization
TL;DR: Multi-LexSum is a multi-doc summarization dataset for civil rights litigations lawsuits with summaries of three granularities.
Abstract: With the advent of large language models, methods for abstractive summarization have made great strides, creating potential for use in applications to aid knowledge workers processing unwieldy document collections. One such setting is the Civil Rights Litigation Clearinghouse (CRLC,, which posts information about large-scale civil rights lawsuits, serving lawyers, scholars, and the general public. Today, summarization in the CRLC requires extensive training of lawyers and law students who spend hours per case understanding multiple relevant documents in order to produce high-quality summaries of key events and outcomes. Motivated by this ongoing real-world summarization effort, we introduce Multi-LexSum, a collection of 9,280 expert-authored summaries drawn from ongoing CRLC writing. Multi-LexSum presents a challenging multi-document summarization task given the length of the source documents, often exceeding two hundred pages per case. Furthermore, 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). We present extensive analysis demonstrating that despite the high-quality summaries in the training data (adhering to strict content and style guidelines), state-of-the-art summarization models perform poorly on this task. We release Multi-LexSum for further summarization research and to facilitate the development of applications to assist in the CRLC's mission at
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License: The Multi-LexSum dataset is distributed under the Open Data Commons Attribution License (ODC-By). The case summaries and metadata are licensed under the Creative Commons Attribution License (CC BY-NC), and the source documents are already in the public domain. Commercial users who desire a license for summaries and metadata can contact, which will allow free use but limit summary reposting. The corresponding code for downloading and loading the dataset is licensed under the Apache License 2.0.
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