Temporal reasoning for timeline summarisation in social media

• URL: http://arxiv.org/abs/2501.00152v2
• Date: Tue, 18 Feb 2025 14:02:12 GMT
• Title: Temporal reasoning for timeline summarisation in social media
• Authors: Jiayu Song, Mahmud Akhter, Dana Atzil Slonim, Maria Liakata,
• Abstract summary: We introduce NarrativeReason, a novel dataset focused on temporal relationships among sequential events within narratives. We then combine temporal reasoning with timeline summarisation through a knowledge distillation framework. Experimental results demonstrate that our model achieves superior performance on out-of-domain mental health-related timeline summarisation tasks.
• Score: 9.475065787773017
• License:
• Abstract: 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.

Related papers

• TempoGPT: Enhancing Temporal Reasoning via Quantizing Embedding [13.996105878417204]
  We propose a multi-modal time series data construction approach and a multi-modal time series language model (TLM), TempoGPT. We construct multi-modal data for complex reasoning tasks by analyzing the variable-system relationships within a white-box system. Extensive experiments demonstrate that TempoGPT accurately perceives temporal information, logically infers conclusions, and achieves state-of-the-art in the constructed complex time series reasoning tasks.
  arXiv  Detail & Related papers  (2025-01-13T13:47:05Z)
• Back to the Future: Towards Explainable Temporal Reasoning with Large Language Models [33.8108950744839]
  We introduce the first task of explainable temporal reasoning, to predict an event's occurrence at a future timestamp based on context. We show that our method achieves the state-of-the-art performance of temporal prediction and explanation.
  arXiv  Detail & Related papers  (2023-10-02T10:35:23Z)
• An Overview Of Temporal Commonsense Reasoning and Acquisition [20.108317515225504]
  Temporal commonsense reasoning refers to the ability to understand the typical temporal context of phrases, actions, and events. Recent research on the performance of large language models suggests that they often take shortcuts in their reasoning and fall prey to simple linguistic traps.
  arXiv  Detail & Related papers  (2023-07-28T01:30:15Z)
• Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic [84.59255070520673]
  Large language models (LLMs) face a challenge when engaging in temporal reasoning. We propose TempLogic, a novel framework designed specifically for temporal question-answering tasks.
  arXiv  Detail & Related papers  (2023-05-24T10:57:53Z)
• Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order [78.46986998674181]
  We propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.
  arXiv  Detail & Related papers  (2023-01-02T20:29:40Z)
• Generic Temporal Reasoning with Differential Analysis and Explanation [61.96034987217583]
  We introduce a novel task named TODAY that bridges the gap with temporal differential analysis. TODAY evaluates whether systems can correctly understand the effect of incremental changes. We show that TODAY's supervision style and explanation annotations can be used in joint learning.
  arXiv  Detail & Related papers  (2022-12-20T17:40:03Z)
• An Empirical Study: Extensive Deep Temporal Point Process [61.14164208094238]
  We first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process. We propose a Granger causality discovery framework for exploiting the relations among multi-types of events.
  arXiv  Detail & Related papers  (2021-10-19T10:15:00Z)
• Temporal Reasoning on Implicit Events from Distant Supervision [91.20159064951487]
  We propose a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events. We find that state-of-the-art models struggle when predicting temporal relationships between implicit and explicit events. We propose a neuro-symbolic temporal reasoning model, SYMTIME, which exploits distant supervision signals from large-scale text and uses temporal rules to infer end times.
  arXiv  Detail & Related papers  (2020-10-24T03:12:27Z)
• A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation [98.25464306634758]
  We propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories. We employ multi-task learning which combines a discriminative objective to distinguish true and fake stories. Our model can generate more reasonable stories than state-of-the-art baselines, particularly in terms of logic and global coherence.
  arXiv  Detail & Related papers  (2020-01-15T05:42:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.