# Improving Automatic Quotation Attribution in Literary Novels

arXiv: https://arxiv.org/abs/2307.03734 · HTML: https://mlsanity.com/paper/2307.03734/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [An Annotated Dataset of Coreference in English Literature](https://mlsanity.com/paper/1912.01140/) (2019)
- [Word-Level Coreference Resolution](https://mlsanity.com/paper/2109.04127/) (2021)
- [Measuring Information Propagation in Literary Social Networks](https://mlsanity.com/paper/2004.13980/) (2020)
- [The Project Dialogism Novel Corpus: A Dataset for Quotation Attribution in Literary Texts](https://mlsanity.com/paper/2204.05836/) (2022)

## Built on by (most cited first, 9 of 9)

- [The Emotion Dynamics of Literary Novels](https://mlsanity.com/paper/2403.02474/) (2024)
- [Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3](https://mlsanity.com/paper/2406.11380/) (2024)
- [Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution](https://mlsanity.com/paper/2401.16968/) (2024)
- [The Elephant in the Coreference Room: Resolving Coreference in Full-Length French Fiction Works](https://mlsanity.com/paper/2510.15594/) (2025)
- [Improving Quotation Attribution with Fictional Character Embeddings](https://mlsanity.com/paper/2406.11368/) (2024)
- [GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study](https://mlsanity.com/paper/2605.28643/) (2026)
- [Identifying Speakers and Addressees of Quotations in Novels with Prompt Learning](https://mlsanity.com/paper/2408.09452/) (2024)
- [Attribution Bias in Large Language Models](https://mlsanity.com/paper/2604.05224/) (2026)
- [Fast and Accurate Quotation Attribution in Literary Texts](https://mlsanity.com/paper/2608.02359/) (2026)

## Abstract (arXiv)

Current models for quotation attribution in literary novels assume varying levels of available information in their training and test data, which poses a challenge for in-the-wild inference. Here, we approach quotation attribution as a set of four interconnected sub-tasks: character identification, coreference resolution, quotation identification, and speaker attribution. We benchmark state-of-the-art models on each of these sub-tasks independently, using a large dataset of annotated coreferences and quotations in literary novels (the Project Dialogism Novel Corpus). We also train and evaluate models for the speaker attribution task in particular, showing that a simple sequential prediction model achieves accuracy scores on par with state-of-the-art models.