Recent incidents with autonomous vehicles highlight the need for rigorous testing to ensure safety and robustness. Constructing test scenarios for autonomous driving systems (ADSs), however, is labor-intensive. We propose TARGET, an end-to-end framework that automatically generates test scenarios from traffic rules. To address complexity, we leverage a Large Language Model (LLM) to extract knowledge from traffic rules. To mitigate hallucinations caused by large context during input processing, we introduce a domain-specific language (DSL) designed to be syntactically simple and compositional. This design allows the LLM to learn and generate test scenarios in a modular manner while enabling syntactic and semantic validation for each component. Based on these validated representations, TARGET synthesizes executable scripts to render scenarios in simulation. Evaluated seven ADSs with 284 scenarios derived from 54 traffic rules, TARGET uncovered 610 rule violations, collisions, and other issues. For each violation, TARGET generates scenario recordings and detailed logs, aiding root cause analysis. Two identified issues were confirmed by ADS developers: one linked to an existing bug report and the other to limited ADS functionality.

Personalized news headline generation aims to sum-marize a news article as a news headline, according to the preference of a specific user. It can help users filter their interested news quickly and increase the news click rates for news providers. However, in this field, when learning user interests from their historically clicked news, existing research only learned user interests on word and news level, ignoring sentence level informativeness. This paper proposes a user model, adding sentence-level informativeness to learn user interests, and further guide the news headline generation. To be more detailed, based on attention layers, sentence and news are represented as the weighted sum of word and sentence representations, respectively. To further explore the correlation between different news contents (news title, body, and topic information), the query vectors in the attention layers are replaced by news content. Experiments on the dataset PENS show that the performance of these two models is better than the baseline model on the evaluation metrics ROUGE. Finally, some future directions are discussed, including interactions across informativeness levels and contents.