Cross-Document Non-Fiction Narrative Alignment

This paper describes a new method for narrative frame alignment that extends and supplements models reliant on graph theory from the domain of fiction to the domain of nonfiction news articles. Preliminary tests of this method against a corpus of 24 articles related to private security firms operating in Iraq and the Blackwater shooting of 2007 show that prior methods utilizing a graph similarity approach can work but require a narrower entity set than commonly occurs in non-fiction texts. They also show that alignment procedures sensitive to abstracted event sequences can accurately highlight similar narratological moments across documents despite syntactic and lexical differences. Evaluation against LDA for both the event sequence lists and source sentences is provided for performance comparison. Next steps include merging these semantic and graph analytic approaches and expanding the test corpus.