Automatic summarization of legal case judgments is a practically important
problem that has attracted substantial research efforts in many countries. In
the context of the Indian judiciary, there is an additional complexity --
Indian legal case judgments are mostly written in complex English, but a
significant portion of India's population lacks command of the English
language. Hence, it is crucial to summarize the legal documents in Indian
languages to ensure equitable access to justice. While prior research primarily
focuses on summarizing legal case judgments in their source languages, this
study presents a pioneering effort toward cross-lingual summarization of
English legal documents into Hindi, the most frequently spoken Indian language.
We construct the first high-quality legal corpus comprising of 3,122 case
judgments from prominent Indian courts in English, along with their summaries
in both English and Hindi, drafted by legal practitioners. We benchmark the
performance of several diverse summarization approaches on our corpus and
demonstrate the need for further research in cross-lingual summarization in the
legal domain.

印度司法体系中，针对印度语言的法律文件进行自动摘要是一个重要的问题，本研究以英语为源语言，尝试将印度的法律文件进行跨语言的英汉自动摘要。研究使用了 3122 例高质量的印度法院的判决案件，并展示了在法律领域跨语言摘要研究的必要性。

MILDSum：用于印度法律案例判决的多语言摘要的新型基准数据集

MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of  Indian Legal Case Judgments

Most legal text in the Indian judiciary is written in complex English due to
historical reasons. However, only about 10% of the Indian population is
comfortable in reading English. Hence legal text needs to be made available in
various Indian languages, possibly by translating the available legal text from
English. Though there has been a lot of research on translation to and between
Indian languages, to our knowledge, there has not been much prior work on such
translation in the legal domain. In this work, we construct the first
high-quality legal parallel corpus containing aligned text units in English and
nine Indian languages, that includes several low-resource languages. We also
benchmark the performance of a wide variety of Machine Translation (MT) systems
over this corpus, including commercial MT systems, open-source MT systems and
Large Language Models. Through a comprehensive survey by Law practitioners, we
check how satisfied they are with the translations by some of these MT systems,
and how well automatic MT evaluation metrics agree with the opinions of Law
practitioners.

印度司法体系的大部分法律文本由于历史原因以复杂的英语编写而成，然而只有约 10% 的印度人能够阅读英语，因此需要将法律文本转化成各种印度语言，考虑到可用的英文法律文本的翻译。虽然对于印度语言之间的翻译已经有了很多研究，但据我们所知，在法律领域中对于这种翻译的先前工作并不多。在本研究中，我们构建了第一个高质量的法律平行语料库，其中包含了英语和九种印度语言的对齐文本单元，包括一些资源稀缺的语言。我们还对该语料库上的各种机器翻译系统进行了性能评估，其中包括商业机器翻译系统、开源机器翻译系统和大型语言模型。通过对法律从业人员的全面调查，我们了解了他们对其中一些机器翻译系统的满意度，并比较了自动机器翻译评估指标与法律从业人员意见之间的一致性。