The term "event extraction" covers a wide range of information extraction tasks, and methods developed and evaluated for one task may prove quite unsuitable for another. Understanding these task differences is essential to making broad progress in event extraction. We look back at the MUC and ACE tasks in terms of one characteristic, the breadth of the scenario - how wide a range of information is subsumed in a single extraction task. We examine how this affects strategies for collecting information and methods for semi-supervised training of new extractors. We also consider the heterogeneity of corpora - how varied the topics of documents in a corpus are. Extraction systems may be intended in principle for general news but are typically evaluated on topic-focused corpora, and this evaluation context may affect system design. As one case study, we examine the task of identifying physical attack events in news corpora, observing the effect on system performance of shifting from an attack-event-rich corpus to a more varied corpus and considering how the impact of this shift may be mitigated.