Text summarization systems face significant adaptation costs when deployed across diverse domains, requiring expensive few-shot learning or manual prompt engineering. We propose a cost-effective domain adaptation framework that generates reusable summarization guidelines using only two reference summaries and three LLM inferences. Our approach works by having the model compare its own generated summaries against domain specific reference summaries in a one time preparation step that derives concise natural language guidelines that capture the summarization patterns of the target domain. These guidelines are then appended to the summarization prompt to adapt the LLM to the target domain at a minimal cost. We evaluate our method across diverse model sizes on three distinct summarization domains: Lawsuits, ArXiv papers, and Patents. Automatic metrics show that guideline-based adaptation achieves comparable or superior performance compared to in-context learning and zero-shot baselines. An LLM preference evaluation using the latest models shows that summaries generated using such guidelines are superior to the zero-shot or in-context learning summarization prompts. Our method enables efficient domain adaptation of text summarizer LLMs with a minimal resource overhead, making specialized summarization particularly accessible for agentic systems that require to process heterogeneous texts in enterprise environments.

The widespread use of the Internet and the rapid dissemination of information poses the challenge of identifying the veracity of its content. Stance detection, which is the task of predicting the position of a text in regard to a specific target (e.g. claim or debate question), has been used to determine the veracity of information in tasks such as rumor classification and fake news detection. While most of the work and available datasets for stance detection address short texts snippets extracted from textual dialogues, social media platforms, or news headlines with a strong focus on the English language, there is a lack of resources targeting long texts in other languages. Our contribution in this paper is twofold. First, we present a German dataset of debate questions and news articles that is manually annotated for stance and emotion detection. Second, we leverage the dataset to tackle the supervised task of classifying the stance of a news article with regards to a debate question and provide baseline models as a reference for future work on stance detection in German news articles.