Predicting Salient Updates for Disaster Summarization

Chris Kedzie, Kathleen McKeown, Fernando Diaz


Abstract

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.