# Tell, Don't Show: Leveraging Language Models' Abstractive Retellings to Model Literary Themes

arXiv: https://arxiv.org/abs/2505.23166 · HTML: https://mlsanity.com/paper/2505.23166/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 76 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 17 of 17)

- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (2019)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- [Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence](https://mlsanity.com/paper/2004.03974/) (2020)
- [Concept Induction: Analyzing Unstructured Text with High-Level Concepts Using LLooM](https://mlsanity.com/paper/2404.12259/) (2024)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [Describing Differences between Text Distributions with Natural Language](https://mlsanity.com/paper/2201.12323/) (2022)
- [Neural Models for Documents with Metadata](https://mlsanity.com/paper/1705.09296/) (2017)
- [Extracting memorized pieces of (copyrighted) books from open-weight language models](https://mlsanity.com/paper/2505.12546/) (2025)
- [Exploring Content Selection in Summarization of Novel Chapters](https://mlsanity.com/paper/2005.01840/) (2020)
- [Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge](https://mlsanity.com/paper/1611.10277/) (2016)
- [Natural Language Decompositions of Implicit Content Enable Better Text Representations](https://mlsanity.com/paper/2305.14583/) (2023)
- [An Automatic Approach for Document-level Topic Model Evaluation](https://mlsanity.com/paper/1706.05140/) (2017)
- [Computational thematics: Comparing algorithms for clustering the genres of literary fiction](https://mlsanity.com/paper/2305.11251/) (2023)

## Built on by (most cited first, 1 of 1)

- [Evaluating Machine Translation Datasets for Low-Web Data Languages: A Gendered Lens](https://mlsanity.com/paper/2511.03880/) (2025)

## Abstract (arXiv)

Conventional bag-of-words approaches for topic modeling, like latent Dirichlet allocation (LDA), struggle with literary text. Literature challenges lexical methods because narrative language focuses on immersive sensory details instead of abstractive description or exposition: writers are advised to "show, don't tell." We propose Retell, a simple, accessible topic modeling approach for literature. Here, we prompt resource-efficient, generative language models (LMs) to tell what passages show, thereby translating narratives' surface forms into higher-level concepts and themes. By running LDA on LMs' retellings of passages, we can obtain more precise and informative topics than by running LDA alone or by directly asking LMs to list topics. To investigate the potential of our method for cultural analytics, we compare our method's outputs to expert-guided annotations in a case study on racial/cultural identity in high school English language arts books.