During crises such as natural disasters or other human tragedies, information needs of both civilians and responders often re- quire urgent, specialized treatment. Moni- toring and summarizing a text stream dur- ing such an event remains a difficult prob- lem. We present a system for update sum- marization which predicts the salience of sentences with respect to an event and then uses these predictions to directly bias a clustering algorithm for sentence se- lection, increasing the quality of the up- dates. We use novel, disaster-specific features for salience prediction, including geo-locations and language models repre- senting the language of disaster. Our eval- uation on a standard set of retrospective events using ROUGE shows that salience prediction provides a significant improve- ment over other approaches.