Title: Text Segmentation Using Affinity Propagation Anna Kazantseva with Stan Szpakowicz Abstract: Text segmentation -- as the name aptly suggests -- is the task of splitting a document into segments, each characterised by a relatively stable topic. For example, given a transcript of a meeting, one may want to split it into segments according to the points of the agenda. A segmenter's output gives a simple picture of the structure of a document. Text segmentation is therefore a useful intermediate step in many higher-level language processing tasks, such as text summarization, question answering, co-reference resolution and so on. This talk presents a new algorithm for linear test segmentation. It is an adaptation of a state-of-the-art clustering algorithm, Affinity Propagation [*]. The algorithm takes as input a (usually sparse) matrix of pairwise similarities between sentences. It outputs segment boundaries and also segment centres –- sentences which best capture the content/topic of a segment. We tested the algorithm on several demanding benchmark data sets. Even though it employs a very simple similarity metric, it performs on par with or outperforms two state-of-the-art segmenters. [*] Inmar E. Givoni and Brendan J. Frey. "A Binary Variable Model for Affinity Propagation". Neural Computation 21(6), June 2009, 1589-1600.