HyperSumm-RL: A Dialogue Summarization Framework for Modeling Leadership Perception in Social Robots

TL;DR

HyperSumm-RL is a novel NLP framework that summarizes and analyzes long social robot dialogues to understand leadership perception, enabling scalable, interpretable insights into human-robot social dynamics.

Contribution

It introduces a new infrastructure for hypertextual summarization and linking of multi-turn dialogues, supporting relational navigation and leadership style analysis in social robot interactions.

Findings

Enhanced understanding of participant trust and engagement.

Demonstrated utility in interpreting leadership framing over time.

Enabled transparent analysis of social dynamics in HRI.

Abstract

This paper introduces HyperSumm-RL, a hypertext-aware summarization and interaction analysis framework designed to investigate human perceptions of social robot leadership through long-form dialogue. The system utilizes a structured Natural Language Processing (NLP) workflow that combines transformer-based long dialogue summarization, leadership style modeling, and user response analysis, enabling scalable evaluation of social robots in complex human-robot interaction (HRI) settings. Unlike prior work that focuses on static or task-oriented HRI, HyperSumm-RL captures and hypertextually organizes dynamic conversational exchanges into navigable, semantically rich representations which allows researchers to trace interaction threads, identify influence cues, and analyze leadership framing over time. The contributions of this study are threefold: (1) we present a novel infrastructure for summarizing and linking long, multi-turn dialogues using leadership-style taxonomies; (2) we propose an interactive hypertext model that supports relational navigation across conversational themes, participant responses, and robot behavior modes; and (3) we demonstrate the utility of this system in interpreting participant trust, engagement, and expectation shifts during social robot leadership scenarios. The findings reveal how hypertextual workflows can augment HRI research by enabling transparent, interpretable, and semantically grounded analysis of emergent social dynamics.