Challenge: Keyphrases are short phrases that describe a text and have been used for many applications.
Approach: They present 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 . they run multilingual models on the corpus and analyze the results .
Outcome: The proposed dataset shows that it is better than existing models and can capture larger input context.

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Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (2022.findings-naacl)

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Challenge: Existing studies on keyphrase generation on non-English languages haven’t been vastly investigated.
Approach: They propose a retrieval-augmented method for multilingual keyphrase generation that leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages.
Outcome: The proposed model outperforms baselines on non-English keyphrase generation datasets and the proposed model is scalable.
EuroVerdict: A Multilingual Dataset for Verdict Generation Against Misinformation (2025.findings-acl)

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Challenge: a global issue that shapes public discourse shapes opinion and decision-making . many multilingual work has focused on claim verification rather than generating explanatory verdicts .
Approach: They propose a multilingual dataset designed for verdict generation covering eight European languages.
Outcome: The EuroVerdict dataset covers claims, manual verdicts, and supporting evidence . it is compared with other datasets in eight European languages .
EUR-Lex-Sum: A Multi- and Cross-lingual Dataset for Long-form Summarization in the Legal Domain (2022.emnlp-main)

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Challenge: Existing summarization datasets focus on overly exposed domains and are primarily monolingual with few multilingual datasets.
Approach: They propose a new summarization dataset based on manually curated document summaries from the European Union law platform EUR-Lex.
Outcome: The proposed dataset is based on document summaries of legal acts from the European Union law platform (EUR-Lex).
MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer (2021.emnlp-main)

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Challenge: MULTI-EURLEX is a dataset for topic classification of EU legal documents . fine-tuning a multilingually pretrained model in a single source language leads to catastrophic forgetting of multilingual knowledge and poor zero-shot transfer to other languages.
Approach: They propose to use the dataset as a testbed for zero-shot cross-lingual transfer to exploit annotated training documents in one language to classify documents in another language.
Outcome: The proposed model can be used to classify EU legal documents in other languages without a single source language and retain multilingual knowledge.
Zero-Shot Keyphrase Generation: Investigating Specialized Instructions and Multi-sample Aggregation on Large Language Models (2025.findings-naacl)

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Challenge: Keyphrase generation is a long-standing NLP task for automatically generating keyphrases for a given document.
Approach: They propose to use open-source instruction-tuned LLMs for keyphrase generation . they propose task-specific counterparts to self-consistency-style strategies for LLM .
Outcome: The proposed model improves on existing models and shows significant benefits over baselines.
LexKeyPlan: Planning with Keyphrases and Retrieval Augmentation for Legal Text Generation: A Case Study on European Court of Human Rights Cases (2025.acl-short)

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Challenge: Large language models excel at text generation but often produce hallucinations due to their sole reliance on parametric knowledge.
Approach: They propose a framework that integrates anticipatory planning into legal text generation by generating keyphrases outlining future content serving as forward-looking plan.
Outcome: The proposed framework improves factual accuracy and coherence by retrieving information aligned with the intended content.
LexCLiPR: Cross-Lingual Paragraph Retrieval from Legal Judgments (2025.acl-long)

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Challenge: Existing work on IR focus on retrieving entire cases rather than precise, paragraph-level information.
Approach: They propose a cross-lingual dataset for paragraph-level retrieval from ECtHR judgments . they evaluate retrieval models in a zero-shot setting and use multilingual case law guides .
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The EuroPat Corpus: A Parallel Corpus of European Patent Data (2022.lrec-1)

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Challenge: a new corpus of patent-specific parallel data is available for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish.
Approach: They present a patent-specific corpus of parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish.
Outcome: The filtered corpus ranges in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpus being up to 2 times larger.
The DReaM Corpus: A Multilingual Annotated Corpus of Grammars for the World’s Languages (2020.lrec-1)

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Challenge: Until recently, language descriptions were available in paper form only, with indexes as the only search aid.
Approach: They propose to digitize a multilingual corpus of language descriptions and annotate it with various meta, word, and text attributes to make searching and analysis easier and more useful.
Outcome: The proposed corpus is searchable through a couple of well-established corpus infrastructures.
Keyphrase Generation for Scientific Document Retrieval (2020.acl-main)

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Challenge: Sequence-to-sequence models have been used to generate keyphrases, but it is unclear whether they are reliable enough for document retrieval.
Approach: They propose a framework for extrinsic evaluation that allows for a better understanding of the limitations of keyphrase generation models.
Outcome: The proposed models improve retrieval performance by supplementing documents with keyphrases that are not present in the source text and generalizing models across domains.

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