  The news coverage of events often contains not one but multiple incompatible
accounts of what happened. We develop a query-based system that extracts
compatible sets of events (scenarios) from such data, formulated as one-class
clustering. Our system incrementally evaluates each event's compatibility with
already selected events, taking order into account. We use synthetic data
consisting of article mixtures for scalable training and evaluate our model on
a new human-curated dataset of scenarios about real-world news topics. Stronger
neural network models and harder synthetic training settings are both important
to achieve high performance, and our final scenario construction system
substantially outperforms baselines based on prior work.
