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Breaking down a document or a conversation into multiple contiguous segmentsbased on its semantic structure is an important and challenging problem in NLP,which can assist many downstream tasks. However, current works on topicsegmentation often focus on segmentation of structured texts. In this paper, wecomprehensively analyze the generalization capabilities of state-of-the-arttopic segmentation models on unstructured texts. We find that: (a) Currentstrategies of pre-training on a large corpus of structured text such asWiki-727K do not help in transferability to unstructured texts. (b) Trainingfrom scratch with only a relatively small-sized dataset of the targetunstructured domain improves the segmentation results by a significant margin.

Topic Segmentation in the Wild: Towards Segmentation of Semi-structured & Unstructured Chats