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

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6809 - 6821 December 7-11, 2022 ©2022 Association for Computational Linguistics Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies Eitan Wagner† Renana Keydar‡ Amit Pinchevski♢ Omri Abend† † Department of Computer Science ‡ Faculty of Law and Digital Humanities ♢ Department of Communication and Journalism Hebrew University of Jerusalem {first_name}.{last_name}@mail.huji.ac.il Abstract 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, chap- ters, or segmenting text that originated from multiple sources. We tackle the task of seg- menting running (spoken) narratives, which poses hitherto unaddressed challenges. As a test case, we address Holocaust survivor testi- monies, given in English. Other than the impor- tance of studying these testimonies for Holo- caust research, we argue that they provide an interesting test case for topical segmentation, due to their unstructured surface level, rela- tive abundance (tens of thousands of such testi- monies were collected), and the relatively con- fined domain that they cover. We hypothesize that boundary points between segments corre- spond to low mutual information between the sentences proceeding and following the bound- ary. 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.1 1 Introduction Proper representation of narratives in long texts re- mains an open problem in NLP (Piper et al., 2021; Castricato et al., 2021; Mikhalkova et al., 2020). High-quality representations for long texts seem crucial to the development of document-level text understanding technology, which is currently unsat- isfactory (Shaham et al., 2022). A common modern approach for modeling narratives is as a sequence of neural states (Wilmot and Keller, 2020, 2021; Rashkin et al., 2020). However, a drawback of this 1Code is provided at https://github.com/ eitanwagner/holocaust-segmentation. Figure 1: Topical segmentation in Holocaust testi- monies. approach is the lack of interpretability, which is crucial in some contexts. A different approach represents and visualizes a narrative as a sequence of interpretable topics (Antoniak et al., 2019). Inspired by this approach, we seek to model the narrative of a text using topic segmentation, dividing long texts into topically co- herent segments and labeling them, thus creating a global topical structure in the form of a chain of topics. Topic segmentation can be useful for the indexing of a large number of testimonies (tens of thousands of testimonies have been collected thus far) and as an intermediate or auxiliary step in tasks such as summarization (Wu et al., 2021) and event detection (Wang et al., 2021). Unlike recent supervised segmentation mod- els that focus on structured written text, such as Wikipedia sections (Arnold et al., 2019; Lukasik et al., 2020) or book chapters (Pethe et al., 2020), we address the hitherto …

Content truncated to 500 words