Official court press releases from Germany's highest courts present and explain judicial rulings to the public, as well as to expert audiences. Prior NLP efforts emphasize technical headnotes, ignoring citizen-oriented communication needs. We introduce CourtPressGER, a 6.4k dataset of triples: rulings, human-drafted press releases, and synthetic prompts for LLMs to generate comparable releases. This benchmark trains and evaluates LLMs in generating accurate, readable summaries from long judicial texts. We benchmark small and large LLMs using reference-based metrics, factual-consistency checks, LLM-as-judge, and expert ranking. Large LLMs produce high-quality drafts with minimal hierarchical performance loss; smaller models require hierarchical setups for long judgments. Initial benchmarks show varying model performance, with human-drafted releases ranking highest.

The Munich court found that OpenAI, the maker of ChatGPT, was not entitled to use song lyrics to train its artificial intelligence without licenses, and that the artists who wrote them are entitled to compensation. The Munich court found that the maker of ChatGPT was not entitled to use song lyrics to train its artificial intelligence without licenses, and that the artists who wrote them are entitled to compensation. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

Songs used by ChatGPT included Herbert Grönemeyer's 1984 synth-pop sendup of masculinity, ' (Men). Songs used by ChatGPT included Herbert Grönemeyer's 1984 synth-pop sendup of masculinity, ' (Men). OpenAI ordered to pay undisclosed damages for training its language models on artists' work without permission The Munich regional court sided in favour of Germany's music rights society GEMA, which said ChatGPT had harvested protected lyrics by popular artists to "learn" from them. The collecting society GEMA, which manages the rights of composers, lyricists and music publishers and has approximately 100,000 members, filed the case against OpenAI in November 2024. The lawsuit was seen as a key European test case in a campaign to stop AI scraping of creative output.

Flights have resumed at Germany's Munich airport after unconfirmed drone sightings forced it to suspend operations for the second time in 24 hours. In a statement on Friday evening, the airport said that flights were stopped at 21:30 local time (20:30 GMT), with around 6,500 passengers affected. At least 17 flights were also grounded in Munich on Thursday evening due to multiple drone sightings in nearby airspace. It was the latest in a series of incidents involving drones that have disrupted aviation in Europe in recent weeks. On Saturday morning, Munich airport said flights had been gradually ramped up, but warned that delays were expected throughout the day.

The clash between billionaire Elon Musk's xAI empire and European officials is intensifying with leaders in Poland and Germany calling for more aggressive action against the company. German lawmaker Ralf Stegner, responding to antisemitic comments that xAI's chatbot Grok made Tuesday on Musk's social media platform, X, said the posts "must not be tolerated under any circumstances" and called for sanctions in an interview with the German newspaper Handelsblatt. Poland's government separately urged the European Union to investigate and possibly fine xAI following lewd comments made by Grok about the country's politicians. The European Union is already investigating Musk's social media platform under a relatively new content-moderation policy known as the Digital Services Act and had been weighing a fine ahead of its summer recess in August. The regulator is reportedly considering calculating the fine by including revenue from Musk's other businesses, including SpaceX and Neuralink, an approach that would significantly increase the potential penalties.

Ukrainian President Volodymyr Zelenskyy is set to meet with German Chancellor Friedrich Merz, as Ukraine seeks further military support amid a recent escalation in Russia's bombing campaign, despite United States-led efforts to end the war. During their talks in Berlin on Wednesday, Zelenskyy and Merz are also expected to discuss sanctions on Russia. According to a German government spokesperson, Merz will receive Zelenskyy with military honours at the federal chancellery at 10:00 GMT. The Berlin talks follow Russia and Ukraine's direct face-to-face talks in Turkiye earlier in May. Despite pressure from United States President Donald Trump to end the war, the talks failed to produce a ceasefire agreement.

Faithfulness evaluators based on large language models (LLMs) are often fooled by the fluency of the text and struggle with identifying errors in the summaries. We propose an approach to summary faithfulness evaluation in which multiple LLM-based agents are assigned initial stances (regardless of what their belief might be) and forced to come up with a reason to justify the imposed belief, thus engaging in a multi-round debate to reach an agreement. The uniformly distributed initial assignments result in a greater diversity of stances leading to more meaningful debates and ultimately more errors identified. Furthermore, by analyzing the recent faithfulness evaluation datasets, we observe that naturally, it is not always the case for a summary to be either faithful to the source document or not. We therefore introduce a new dimension, ambiguity, and a detailed taxonomy to identify such special cases. Experiments demonstrate our approach can help identify ambiguities, and have even a stronger performance on non-ambiguous summaries.

This paper presents a novel dataset of public broadcast interviews featuring high-ranking German politicians. The interviews were sourced from YouTube, transcribed, processed for speaker identification, and stored in a tidy and open format. The dataset comprises 99 interviews with 33 different German politicians across five major interview formats, containing a total of 28,146 sentences. As the first of its kind, this dataset offers valuable opportunities for research on various aspects of political communication in the (German) political contexts, such as agenda-setting, interviewer dynamics, or politicians' self-presentation.

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI's GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs.

As generative Artificial Intelligence (genAI) technologies proliferate across sectors, they offer significant benefits but also risk exacerbating discrimination. This chapter explores how genAI intersects with non-discrimination laws, identifying shortcomings and suggesting improvements. It highlights two main types of discriminatory outputs: (i) demeaning and abusive content and (ii) subtler biases due to inadequate representation of protected groups, which may not be overtly discriminatory in individual cases but have cumulative discriminatory effects. For example, genAI systems may predominantly depict white men when asked for images of people in important jobs. This chapter examines these issues, categorizing problematic outputs into three legal categories: discriminatory content; harassment; and legally hard cases like unbalanced content, harmful stereotypes or misclassification. It argues for holding genAI providers and deployers liable for discriminatory outputs and highlights the inadequacy of traditional legal frameworks to address genAI-specific issues. The chapter suggests updating EU laws, including the AI Act, to mitigate biases in training and input data, mandating testing and auditing, and evolving legislation to enforce standards for bias mitigation and inclusivity as technology advances.