# Temporal reasoning for timeline summarisation in social media

**Authors:** Jiayu Song, Mahmud Akhter, Dana Atzil Slonim, Maria Liakata  
**arXiv:** [2501.00152](https://arxiv.org/abs/2501.00152)  
**Date:** 2024-12-30  
**Summary page:** https://www.summarizepaper.com/en/paper/2501.00152v1/

## Key points

- Authors explore enhancing temporal reasoning capabilities in Large Language Models (LLMs) for timeline summarization
- Introduction of the \textit{NarrativeReason} dataset targeting temporal relationships among sequential events
- Combination of temporal reasoning with timeline summarization through a knowledge distillation framework
- Significant improvements in mental health-related timeline summarization tasks demonstrated by leveraging temporal reasoning capabilities within LLMs

## Summary

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In their paper titled "Temporal Reasoning for Timeline Summarisation in Social Media," authors Jiayu Song, Mahmud Akhter, Dana Atzil Slonim, and Maria Liakata explore the potential benefits of enhancing temporal reasoning capabilities in Large Language Models (LLMs) to improve the quality of timeline summarization. The task at hand involves condensing lengthy texts that contain sequences of events, with a focus on social media threads. The researchers introduce the \textit{NarrativeReason} dataset, which specifically targets temporal relationships among sequential events within narratives. This new dataset serves as a valuable resource for studying temporal reasoning and sets itself apart from existing datasets that mainly address pair-wise event relationships. The approach taken by the authors combines temporal reasoning with timeline summarization through a knowledge distillation framework. Initially, a teacher model is fine-tuned on temporal reasoning tasks before transferring this knowledge to a student model. This transfer occurs concurrently with training the student model for the task of timeline summarization. Experimental results showcase the effectiveness of this method, particularly in mental health-related timeline summarization tasks involving long social media threads characterized by event repetitions and diverse emotional content. By leveraging temporal reasoning capabilities within LLMs, the researchers demonstrate significant improvements in timeline summarization quality. Their findings underscore the importance of incorporating temporal relationships into text summarization processes, especially when dealing with complex narratives like those found in social media threads. This study contributes valuable insights to the field of natural language processing and highlights the potential for enhanced narrative understanding through advanced temporal reasoning techniques.