To enhance the generation of SCDs and ensure they effectively capture the nuances of conversation dynamics, several strategies can be implemented:
Fine-tuning Models: Fine-tuning large language models (LLMs) on a more extensive dataset of human-written SCDs can help the models better understand and replicate the nuanced aspects of conversation dynamics present in the summaries.
Multimodal Approach: Incorporating multimodal elements such as vocal features, gestures, or facial expressions into the summarization process can provide a more comprehensive understanding of the conversation dynamics beyond just text-based interactions.
Incorporating Contextual Cues: Developing models that can understand and incorporate contextual cues from the conversation, such as previous interactions, emotional states of participants, or historical context, can lead to more accurate and contextually relevant SCDs.
Interactive Learning: Implementing interactive learning mechanisms where the model can receive feedback on generated summaries from human annotators or users can help refine the model's understanding of conversation dynamics over time.
Attention Mechanisms: Leveraging attention mechanisms to focus on specific elements of the conversation, such as changes in tone, conversational strategies, or patterns of interaction, can improve the model's ability to capture these nuances in the generated summaries.
By integrating these strategies and continuously refining the models based on feedback and evaluation, the generation of SCDs can be enhanced to provide more insightful and nuanced summaries of conversation dynamics.