The task of topical segmentation is well studied, but previous work has
mostly addressed it in the context of structured, well-defined segments, such
as segmentation into paragraphs, chapters, or segmenting text that originated
from multiple sources. We tackle the task of segmenting running (spoken)
narratives, which poses hitherto unaddressed challenges. As a test case, we
address Holocaust survivor testimonies, given in English. Other than the
importance of studying these testimonies for Holocaust research, we argue that
they provide an interesting test case for topical segmentation, due to their
unstructured surface level, relative abundance (tens of thousands of such
testimonies were collected), and the relatively confined domain that they
cover. We hypothesize that boundary points between segments correspond to low
mutual information between the sentences proceeding and following the boundary.
Based on this hypothesis, we explore a range of algorithmic approaches to the
task, building on previous work on segmentation that uses generative Bayesian
modeling and state-of-the-art neural machinery. Compared to manually annotated
references, we find that the developed approaches show considerable
improvements over previous work.

本文研究如何将长篇英语口述叙述中的段落进行主题划分，以大量收集的 Holocaust 幸存者证言为例，通过使用诸如贝叶斯模型和神经算法等算法，成功地将其与手动标记参考资料做了比较，展示了比以往研究更高的水平。