Keyphrase generation has primarily been explored within the context of
academic research articles, with a particular focus on scientific domains and
the English language. In this work, we present EUROPA, a dataset for
multilingual keyphrase generation in the legal domain. It is derived from legal
judgments from the Court of Justice of the European Union (EU), and contains
instances in all 24 EU official languages. We run multilingual models on our
corpus and analyze the results, showing room for improvement on a
domain-specific multilingual corpus such as the one we present.

在法律领域中，我们提供了 EUROPA 数据集来支持多语言关键短语生成，利用欧洲联盟 (EU) 法院的法律判决作为数据源，涵盖了所有 24 种 EU 官方语言。我们在此数据集上运行了多语言模型并分析了结果，结果显示在特定领域的多语言语料库上仍有改进的空间。

EUROPA: 法律多语种关键词生成数据集

EUROPA: A Legal Multilingual Keyphrase Generation Dataset

Keyphrase generation is the task of automatically predicting keyphrases given
a piece of long text. Despite its recent flourishing, keyphrase generation on
non-English languages haven't been vastly investigated. In this paper, we call
attention to a new setting named multilingual keyphrase generation and we
contribute two new datasets, EcommerceMKP and AcademicMKP, covering six
languages. Technically, we propose a retrieval-augmented method for
multilingual keyphrase generation to mitigate the data shortage problem in
non-English languages. The retrieval-augmented model leverages keyphrase
annotations in English datasets to facilitate generating keyphrases in
low-resource languages. Given a non-English passage, a cross-lingual dense
passage retrieval module finds relevant English passages. Then the associated
English keyphrases serve as external knowledge for keyphrase generation in the
current language. Moreover, we develop a retriever-generator iterative training
algorithm to mine pseudo parallel passage pairs to strengthen the cross-lingual
passage retriever. Comprehensive experiments and ablations show that the
proposed approach outperforms all baselines.

该论文介绍了一种新的情景，名为多语言关键短语生成，并提出了一种基于检索增强的方法来缓解非英语语言中数据短缺的问题，使用检索模型找到相关的英文段落并生成当前语言的关键短语。实验结果表明，所提出的方法优于所有基线模型。