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The task of topical segmentation is well studied, but previous work hasmostly addressed it in the context of structured, well-defined segments, suchas segmentation into paragraphs, chapters, or segmenting text that originatedfrom multiple sources. We tackle the task of segmenting running (spoken)narratives, which poses hitherto unaddressed challenges. As a test case, weaddress Holocaust survivor testimonies, given in English. Other than theimportance of studying these testimonies for Holocaust research, we argue thatthey provide an interesting test case for topical segmentation, due to theirunstructured surface level, relative abundance (tens of thousands of suchtestimonies were collected), and the relatively confined domain that theycover. We hypothesize that boundary points between segments correspond to lowmutual information between the sentences proceeding and following the boundary.Based on this hypothesis, we explore a range of algorithmic approaches to thetask, building on previous work on segmentation that uses generative Bayesianmodeling and state-of-the-art neural machinery. Compared to manually annotatedreferences, we find that the developed approaches show considerableimprovements over previous work.

Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies