Abstract

Abstract
Municipal meeting minutes are official documents of local governance that exhibit heterogeneous formats and writing styles. Effective information retrieval (IR) requires identifying metadata such as meeting number, date, location, participants, and start/end times, elements that are rarely standardized or easily extracted automatically. Existing named entity recognition (NER) models are ill-suited to this task, as they are not adapted to such domain-specific categories. In this paper, we propose a two-stage pipeline for metadata extraction from municipal minutes. First, a question-answering (QA) model identifies the opening and closing text segments containing metadata. Transformer-based models (BERTimbau and XLM-RoBERTa with and without a CRF layer) are then applied for fine-grained entity extraction, with deslexicalization explored as an additional modeling strategy. We benchmark the pipeline against open and closed-weight LLMs (Phi and Gemini), considering performance, inference cost, and carbon footprint. Our results demonstrate strong in-domain performance, outperforming the evaluated LLMs. Differences observed in cross-municipality evaluation highlight the linguistic diversity and structural variation across municipal records, underscoring the challenges of generalization in this domain and motivating future research in metadata extraction from municipal minutes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
This paper summarises the setup and results of the shared task on onomastic wordplay translation at the CLEF 2025 JOKER Lab, an earlier version of which was run at JOKER 2022 as a pilot task. The objective of the task is to translate wordplay concerned with proper names from English to French. Such wordplay, widespread in classic and modern creative writing, is particularly challenging to translate due to its idiosyncratic nature and cultural references. Four teams participated in this year’s task, submitting 20 runs. We describe our construction of the data set using for training and testing, the methods employed by the participating teams, and the results obtained for the runs and a naïve baseline in terms of various manually and automatically applied measures of translation quality. Despite notable advances, we find that translation of onomastic wordplay remains highly challenging, with fewer than 10% of manually evaluated translations judged as acceptable alternatives. Recurrent errors included untranslated source wordplay, overfitting to the training data, omission of surnames, and nonsensical generations.

Abstract
This paper presents the details of Task 1 of the JOKER-2025 Track, an information retrieval task where the goal is to find relevant humorous in a collection of text documents. The intended use case is retrieving jokes on a specific topic, something that may benefit humanities research, second-language learning, and the writing or translation of comedic texts. We provide two document collections: one in English and another in European Portuguese. The English collection consists of 77,658 documents, of which 5,198 are annotated as humorous, and 219 queries with relevance judgments. The Portuguese collection contains 45,126 texts, including 1,199 humorous documents along with 98 queries. Together, these collections support cross-linguistic studies in humour detection and contribute to the development of more inclusive and language-aware retrieval systems. Nine teams submitted 62 runs in total for this task.

Abstract
This paper describes Task 2 of the CLEF 2025 JOKER track on the translation of puns from English into French. We outline the overall structure and setup of the shared task, discuss the approaches employed by the participants, and present and analyse the results they achieved. We also describe experiments with a promising new approach for the automatic evaluation of pun translation. Despite the significant improvements observed this year by participating systems, most of which used state-of-the-art large language models, we find wordplay translation to remain a complex and demanding task. Among the manually evaluated translations, 37.5% successfully preserved the meaning and involved wordplay, with success rates per English pun varying widely.

Abstract
City councils play a crucial role in local governance, directly influencing citizens’ daily lives through decisions made during municipal meetings. These deliberations are formally documented in meeting minutes, which serve as official records of discussions, decisions, and voting outcomes. Despite their importance, municipal meeting records have received little attention in Information Retrieval (IR) and Natural Language Processing (NLP), largely due to the lack of annotated datasets, which ultimately limit the development of computational models. To address this gap, we introduce CitiLink-Minutes, a multilayer dataset of 120 European Portuguese municipal meeting minutes from six municipalities. Unlike prior annotated datasets of parliamentary or video records, CitiLink-Minutes provides multilayer annotations and structured linkage of official written minutes. The dataset contains over one million tokens, with all personal identifiers de-identified. Each minute was manually annotated by two trained annotators and curated by an experienced linguist across four complementary dimensions: (1) personal information, (2) metadata, (3) subjects of discussion, and (4) voting outcomes, totaling over 38,000 individual annotations. Released under FAIR principles and accompanied by baseline results on metadata extraction, topic classification, and vote labeling, CitiLink-Minutes demonstrates its potential for downstream NLP and IR tasks, while promoting transparent access to municipal decisions.