# Inferring Interpersonal Relations in Narrative Summaries

arXiv: https://arxiv.org/abs/1512.00112 · HTML: https://mlsanity.com/paper/1512.00112/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2015.

## Built on by (most cited first, 16 of 16)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [MBTI Personality Prediction for Fictional Characters Using Movie Scripts](https://mlsanity.com/paper/2210.10994/) (2022)
- [Towards Inter-character Relationship-driven Story Generation](https://mlsanity.com/paper/2211.00676/) (2022)
- [SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues](https://mlsanity.com/paper/2106.01006/) (2021)
- [Grounding Characters and Places in Narrative Texts](https://mlsanity.com/paper/2305.17561/) (2023)
- [An Ordinal Latent Variable Model of Conflict Intensity](https://mlsanity.com/paper/2210.03971/) (2022)
- [Detecting and Grounding Important Characters in Visual Stories](https://mlsanity.com/paper/2303.17647/) (2023)
- [Classifying Dyads for Militarized Conflict Analysis](https://mlsanity.com/paper/2109.12860/) (2021)
- [Classifying Unreliable Narrators with Large Language Models](https://mlsanity.com/paper/2506.10231/) (2025)
- [DiscoGraMS: Enhancing Movie Screen-Play Summarization using Movie Character-Aware Discourse Graph](https://mlsanity.com/paper/2410.14666/) (2024)
- [Computational Storytelling and Emotions: A Survey](https://mlsanity.com/paper/2205.10967/) (2022)
- [NarrationDep: Narratives on Social Media For Automatic Depression Detection](https://mlsanity.com/paper/2407.17174/) (2024)
- [Taggus: An Automated Pipeline for the Extraction of Characters' Social Networks from Portuguese Fiction Literature](https://mlsanity.com/paper/2508.03358/) (2025)
- [MARCUS: An Event-Centric NLP Pipeline that generates Character Arcs from Narratives](https://mlsanity.com/paper/2510.18201/) (2025)
- [CASPER in the Machine: Insights into Character Variety in LLM-Generated Stories](https://mlsanity.com/paper/2606.22454/) (2026)

## Abstract (arXiv)

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation.