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)
(this https URL),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 research in summarization methods as well
as to facilitate development of applications to assist in the CRLC's mission at
this https URL.

我们介绍了 Multi-LexSum，它是一个包含 9,280 篇专家撰写的摘要的集合，用于进行多文档摘要生成的挑战性任务，并且尽管训练数据的摘要质量很高，但最先进的摘要模型在此任务上表现不佳。

Multi-LexSum: 多粒度民权诉讼实现的现实世界摘要

Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities

Recent models in developing summarization systems consist of millions of
parameters and the model performance is highly dependent on the abundance of
training data. While most existing summarization corpora contain data in the
order of thousands to one million, generation of large-scale summarization
datasets in order of couple of millions is yet to be explored. Practically,
more data is better at generalizing the training patterns to unseen data. In
this paper, we introduce TLDR9+ -- a large-scale summarization dataset --
containing over 9 million training instances extracted from Reddit discussion
forum (this https URL). This dataset is
specifically gathered to perform extreme summarization (i.e., generating
one-sentence summary in high compression and abstraction) and is more than
twice larger than the previously proposed dataset. We go one step further and
with the help of human annotations, we distill a more fine-grained dataset by
sampling High-Quality instances from TLDR9+ and call it TLDRHQ dataset. We
further pinpoint different state-of-the-art summarization models on our
proposed datasets.

本文介绍了 TLDR9 +—— 一个从 Reddit 讨论论坛中提取的包含 900 万多个训练实例的大规模摘要数据集，旨在进行极端摘要，并通过人工注释蒸馏出更细粒度的 TLDRHQ 数据集，进一步点出我们所提出的数据集上不同的最先进的摘要模型。