标题： 社交媒体时间线摘要的时序推理 显示英文标题

标题： Temporal reasoning for timeline summarisation in social media

摘要： 本文探讨了增强大型语言模型（LLMs）的时间推理能力是否能够提高时间线摘要的质量，时间线摘要的任务是对包含事件序列的长文本进行摘要，例如社交媒体帖子的连续内容。我们首先介绍了NarrativeReason，这是一个专注于叙事中顺序事件之间时间关系的新数据集，它与现有主要关注成对事件关系的时间推理数据集有所不同。然后，我们的方法通过知识蒸馏框架将时间推理与时间线摘要相结合，我们首先在时间推理任务上微调教师模型，然后在同时训练学生模型进行时间线摘要任务的过程中将其知识蒸馏到学生模型中。实验结果表明，我们的模型在跨领域的心理健康相关时间线摘要任务中表现出色，这些任务涉及包含重复事件和多种情绪的长社交媒体帖子，突显了利用时间推理来改进时间线摘要的重要性和通用性。 显示英文摘要

摘要： This paper explores whether enhancing temporal reasoning capabilities in Large Language Models (LLMs) can improve the quality of timeline summarisation, the task of summarising long texts containing sequences of events, such as social media threads. We first introduce NarrativeReason, a novel dataset focused on temporal relationships among sequential events within narratives, distinguishing it from existing temporal reasoning datasets that primarily address pair-wise event relationships. Our approach then combines temporal reasoning with timeline summarisation through a knowledge distillation framework, where we first fine-tune a teacher model on temporal reasoning tasks and then distill this knowledge into a student model while simultaneously training it for the task of timeline summarisation. Experimental results demonstrate that our model achieves superior performance on out-of-domain mental health-related timeline summarisation tasks, which involve long social media threads with repetitions of events and a mix of emotions, highlighting the importance and generalisability of leveraging temporal reasoning to improve timeline summarisation.