• Comprehensive Legal Conflict Dataset: Clash-of-Leges introduces a novel dataset that captures authentic legal conflicts between provisions across different documents, derived from Italian Constitutional Court rulings, to support advanced AI research in legal conflict detection and explanation. • Bilingual and Multi-Task Framework: The dataset includes annotations for three key tasks-Conflict Detection, Conflict Explanation Generation, and Reference Retrieval-while offering bilingual support in Italian and English to enhance global research applicability. • Extensive Evalutions: The manuscript resents an extensive experimental evaluation of several large language models across all defined tasks, illustrating both their capabilities and current limitations. Legal conflicts between statutes or constitutional articles present a significant challenge in maintaining consistency and coherence within legal systems. Addressing these conflicts requires extensive human expertise, making the process labor-intensive and time-consuming. In this paper, we introduce Clash-of-Leges , a novel multilingual dataset derived from rulings by the Constitutional Court of the Italian Republic, designed to aid the automation of conflict detection and explanation between legal articles. We identify three key tasks: Conflict Classification, which determines whether two legal articles are in conflict; Conflict Explanation Generation, which provides detailed explanations for identified conflicts; and Reference Retrieval, which sources relevant legal bases and precedents to substantiate interpretations. These tasks are intended to facilitate the development of AI models that can automatically identify and explain contradictions between legal provisions. 2

Alessio Cocchieri, Luca Ragazzi, Paolo Italiani, Giuseppe Tagliavini, Gianluca Moro. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Abstractive dialogue summarization requires distilling and rephrasing key information from noisy multi-speaker documents. Combining pre-trained language models with input augmentation techniques has recently led to significant research progress. However, existing solutions still struggle to select relevant chat segments, primarily relying on open-domain and unsupervised annotators not tailored to the actual needs of the summarization task. In this paper, we propose DearWatson, a task-aware utterance-level annotation framework for improving the effectiveness and interpretability of pre-trained dialogue summarization models. Precisely, we learn relevant utterances in the source document and mark them with special tags, that then act as supporting evidence for the generated summary. Quantitative experiments are conducted on two datasets made up of real-life messenger conversations. The results show that DearWatson allows model attention to focus on salient tokens, achieving new state-of-the-art results in three evaluation metrics, including semantic and factuality measures. Human evaluation proves the superiority of our solution in semantic consistency and recall. Finally, extensive ablation studies confirm each module’s importance, also exploring different annotation strategies and parameter-efficient fine-tuning of large generative language models.

The automatic synthesis of biomedical publications catalyzes a profound research interest elicited by literature congestion. Current sequence-to-sequence models mainly rely on the lexical surface and seldom consider the deep semantic interconnections between the entities mentioned in the source document. Such superficiality translates into fabricated, poorly informative, redundant, and near-extractive summaries that severely restrict their real-world application in biomedicine, where the specialized jargon and the convoluted facts further emphasize task complexity. To fill this gap, we argue that the summarizer should acquire semantic interpretation over input, exploiting structured and unambiguous representations to capture and conserve the most relevant parts of the text content. This paper presents CogitoErgoSumm, the first framework for biomedical abstractive summarization equipping large pre-trained language models with rich semantic graphs. Precisely, we infuse graphs from two complementary semantic parsing techniques with different goals and granularities—Event Extraction and Abstract Meaning Representation, also designing a reward signal to maximize information content preservation through reinforcement learning. Extensive quantitative and qualitative evaluations on the CDSR dataset show that our solution achieves competitive performance according to multiple metrics, despite using 2.5x fewer parameters. Results and ablation studies indicate that our joint text-graph model generates more enlightening, readable, and consistent summaries. Code available at: https://github.com/disi-unibo-nlp/cogito-ergo-summ.

Combining structured knowledge and neural language models to tackle natural language processing tasks is a recent research trend that catalyzes community attention. This integration holds a lot of potential in document summarization, especially in the biomedical domain, where the jargon and the complex facts make the overarching information truly hard to interpret. In this context, graph construction via semantic parsing plays a crucial role in unambiguously capturing the most relevant parts of a document. However, current works are limited to extracting open-domain triples, failing to model real-world n-ary and nested biomedical interactions accurately. To alleviate this issue, we present EASumm, the first framework for biomedical abstractive summarization enhanced by event graph extraction (i.e., graphical representations of medical evidence learned from scientific text), relying on dual text-graph encoders. Extensive evaluations on the CDSR dataset corroborate the importance of explicit event structures, with better or comparable performance than previous state-of-the-art systems. Finally, we offer some hints to guide future research in the field.