Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition

2026-07-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A novel dialogue summarization framework is proposed, designed to explicitly model both semantic and emotion dynamics within multimodal dialogue inputs. This framework adapts a hierarchical Chain-of-Agents approach and decomposes dialogues from two perspectives: topic segments based on all participants' utterances, and participant-specific utterance segments. These decompositions are used to generate corresponding summaries, incorporating automatically inferred emotions. The topic- and participant-level summaries are then aggregated to form a comprehensive dialogue summary that captures both semantic content and emotion trajectories. To evaluate its effectiveness beyond content accuracy, the framework introduces specific emotion trajectory metrics. Experiments conducted with small language models on multimodal dialogue datasets demonstrate that this methodology produces summaries rich in both semantic and emotional content, highlighting opportunities in dialogue analysis using language models.

Key takeaway

For NLP Engineers developing dialogue summarization systems, consider integrating explicit emotion dynamics and participant-centric decomposition. Your summaries will better preserve emotional flow and interaction nuances, moving beyond mere semantic content. This approach, validated with small language models, offers a robust method to enhance the richness and accuracy of dialogue analysis, providing a more complete understanding of conversational exchanges.

Key insights

The framework integrates semantic and emotion dynamics for dialogue summarization using a decomposed, agent-based approach.

Principles

• Dialogue summarization benefits from modeling participant interaction.
• Decomposing dialogues by topic and participant enhances summary quality.
• Emotion dynamics are crucial for comprehensive dialogue understanding.

Method

The framework uses an adapted hierarchical Chain-of-Agents. It decomposes dialogues into topic segments and participant-specific utterance segments, generating summaries with inferred emotions, then aggregates them.

In practice

• Apply topic- and participant-centric decomposition for dialogue analysis.
• Incorporate emotion inference to enrich summarization outputs.
• Use emotion trajectory metrics for evaluating dialogue summaries.

Topics

• Dialogue Summarization
• Emotion Dynamics
• Chain-of-Agents
• Multimodal Dialogue
• Small Language Models
• Natural Language Processing

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.