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

arXiv: https://arxiv.org/abs/2210.13783 · HTML: https://mlsanity.com/paper/2210.13783/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 15 of 15)

- [FNet: Mixing Tokens with Fourier Transforms](https://mlsanity.com/paper/2105.03824/) (2021)
- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [SCROLLS: Standardized CompaRison Over Long Language Sequences](https://mlsanity.com/paper/2201.03533/) (2022)
- [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020)
- [Text Segmentation as a Supervised Learning Task](https://mlsanity.com/paper/1803.09337/) (2018)
- [Text Segmentation by Cross Segment Attention](https://mlsanity.com/paper/2004.14535/) (2020)
- [SECTOR: A Neural Model for Coherent Topic Segmentation and Classification](https://mlsanity.com/paper/1902.04793/) (2019)
- [Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation](https://mlsanity.com/paper/2001.00891/) (2020)
- [Modelling Suspense in Short Stories as Uncertainty Reduction over Neural Representation](https://mlsanity.com/paper/2004.14905/) (2020)
- [Learning Constraints and Descriptive Segmentation for Subevent Detection](https://mlsanity.com/paper/2109.06316/) (2021)
- [Towards a Formal Model of Narratives](https://mlsanity.com/paper/2103.12872/) (2021)
- [A Temporal Variational Model for Story Generation](https://mlsanity.com/paper/2109.06807/) (2021)
- [Mapping Out Narrative Structures and Dynamics Using Networks and Textual Information](https://mlsanity.com/paper/1604.03029/) (2016)
- [BATS: A Spectral Biclustering Approach to Single Document Topic Modeling and Segmentation](https://mlsanity.com/paper/2008.02218/) (2020)
- [Chapter Captor: Text Segmentation in Novels](https://mlsanity.com/paper/2011.04163/) (2020)

## Built on by (most cited first, 4 of 4)

- [PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters](https://mlsanity.com/paper/2410.16148/) (2024)
- [Unsupervised Speech Segmentation: A General Approach Using Speech Language Models](https://mlsanity.com/paper/2501.03711/) (2025)
- [Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling](https://mlsanity.com/paper/2405.02650/) (2024)
- [The Shape of Testimony: A Scalable Framework for Oral History Archive Comparison](https://mlsanity.com/paper/2605.21623/) (2026)

## Abstract (arXiv)

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.