Existing human value datasets do not directly support value understanding in factual news: many are actor-agnostic, rely on isolated utterances or synthetic scenarios, and lack explicit event structure or value direction. We present \textbf{ NEVU} (\textbf{ N}ews \textbf{ E}vent-centric \textbf{ V}alue \textbf{ U}nderstanding), a benchmark for \emph{ actor-conditioned}, \emph{ event-centric}, and \emph{ direction-aware} human value recognition in factual news. NEVU evaluates whether models can identify value cues, attribute them to the correct actor, and determine value direction from grounded evidence. Built from 2{ ,}865 English news articles, NEVU organizes annotations at four semantic unit levels (\textbf{ Subevent}, \textbf{ behavior-based composite event}, \textbf{ story-based composite event}, and \textbf{ Article}) and labels \mbox{ (unit, actor)} pairs for fine-grained evaluation across local and composite contexts. The annotations are produced through an LLM-assisted pipeline with staged verification and targeted human auditing. Using a hierarchical value space with \textbf{ 54} fine-grained values and \textbf{ 20} coarse-grained categories, NEVU covers 45{ ,}793 unit--actor pairs and 168{ ,}061 directed value instances. We provide unified baselines for proprietary and open-source LLMs, and find that lightweight adaptation (LoRA) consistently improves open-source models, showing that although NEVU is designed primarily as a benchmark, it also supports supervised adaptation beyond prompting-only evaluation. Data availability is described in Appendix~\ref{ app:data_code_availability}.
Categories: cs.CL