Automatic generation of crisis situation reports from social media requires transforming noisy, short-form text into structured, evidence-grounded prose, without access to information outside a single bounded time window. This paper describes an extractive pipeline submitted to a shared task on this problem: a supervised section classifier trained on tweet-to-bullet citation links, corroboration-based confidence estimation, safety-netted extractive bullet-text synthesis, and rule-based temporal filtering. The submitted system passes the task’s structural validator on all official test cells, spanning crisis types absent from training, and receives an official evaluation score of 0.480. Decomposing the two metrics composing this score shows that the system obtains a BERTScore F1 of 0.508 and a BLEURT score of 0.451, exposing a substantial difference between semantic similarity and learned fluency/adequacy evaluation. Development-time diagnostics, including a coreference-recall experiment and a per-section quality breakdown, provide independent evidence that extractive text generation is a major remaining limitation. Three architectural alternatives tested during development are reported with their measurements rather than omitted, and a scoped, safety-netted rewrite step is identified as a direct next architectural extension.