Summary of Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling, by Maxim Ifergan et al.
Identifying Narrative Patterns and Outliers in Holocaust Testimonies Using Topic Modeling
by Maxim Ifergan, Renana Keydar, Omri Abend, Amit Pinchevski
First submitted to arxiv on: 4 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the USC Shoah Foundation Holocaust testimony corpus using advanced Natural Language Processing (NLP) techniques. It treats testimonies as structured question-and-answer sections and applies topic modeling to identify key themes. The authors experiment with BERTopic, leveraging recent advances in language modeling technology. They align testimony sections into fixed parts, revealing the evolution of topics across the corpus of testimonies. This highlights both a common narrative schema and divergences between subgroups based on age and gender. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computer programs to understand huge collection of stories from people who survived the Holocaust. The stories are like long questions and answers, so the program can find what’s important in each story. It finds patterns in how the survivors talk about their experiences, which helps us see what they have in common and what makes them different. This is a new way to understand these important stories and see how people who lived through the Holocaust felt and thought. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
