DTELS: Towards Dynamic Granularity of Timeline Summarization

• URL: http://arxiv.org/abs/2411.09297v1
• Date: Thu, 14 Nov 2024 09:16:48 GMT
• Title: DTELS: Towards Dynamic Granularity of Timeline Summarization
• Authors: Chenlong Zhang, Tong Zhou, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao,
• Abstract summary: We introduce a new paradigm, Dynamic-granularity TimELine Summarization, (DTELS), which aims to construct adaptive timelines based on user instructions or requirements. This paper establishes a comprehensive benchmark for DTLES that includes: (1) an evaluation framework grounded in journalistic standards to assess the timeline quality across four dimensions: Informativeness, Granular Consistency, Factuality, and Coherence; (2) a large-scale, multi-source dataset with multiple granularity timeline annotations based on a consensus process to facilitate authority; and (3) extensive experiments and analysis with two proposed solutions based on Large Language Models (LLMs)
• Score: 23.375978839341855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid proliferation of online news has posed significant challenges in tracking the continuous development of news topics. Traditional timeline summarization constructs a chronological summary of the events but often lacks the flexibility to meet the diverse granularity needs. To overcome this limitation, we introduce a new paradigm, Dynamic-granularity TimELine Summarization, (DTELS), which aims to construct adaptive timelines based on user instructions or requirements. This paper establishes a comprehensive benchmark for DTLES that includes: (1) an evaluation framework grounded in journalistic standards to assess the timeline quality across four dimensions: Informativeness, Granular Consistency, Factuality, and Coherence; (2) a large-scale, multi-source dataset with multiple granularity timeline annotations based on a consensus process to facilitate authority; (3) extensive experiments and analysis with two proposed solutions based on Large Language Models (LLMs) and existing state-of-the-art TLS methods. The experimental results demonstrate the effectiveness of LLM-based solutions. However, even the most advanced LLMs struggle to consistently generate timelines that are both informative and granularly consistent, highlighting the challenges of the DTELS task.

Related papers

• Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model [63.14883657299359]
  Multi-modal Large Language Models (MLLMs) integrate visual and linguistic reasoning to address complex tasks such as image captioning and visual question answering. tuning MLLMs for downstream tasks encounters two key challenges: Task-Expert, where distribution shifts between pre-training and target datasets constrain target performance, and OpenWorld Stabilization, where catastrophic forgetting erases the model general knowledge.
  arXiv  Detail & Related papers  (2025-03-06T15:29:13Z)
• RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery [69.41989381702858]
  Existing methods, such as direct generation and multi-agent discussion, often struggle with issues like hallucinations, topic incoherence, and significant latency. We propose RAPID, an efficient retrieval-augmented long text generation framework. Our work provides a robust and efficient solution to the challenges of automated long-text generation.
  arXiv  Detail & Related papers  (2025-03-02T06:11:29Z)
• Position: Empowering Time Series Reasoning with Multimodal LLMs [49.73647759532127]
  We argue that multimodal language models (MLLMs) can enable more powerful and flexible reasoning for time series analysis. We call on researchers and practitioners to leverage this potential by developing strategies that prioritize trust, interpretability, and robust reasoning in MLLMs.
  arXiv  Detail & Related papers  (2025-02-03T16:10:48Z)
• TempoGPT: Enhancing Time Series 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)
• TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via Large Language Models [54.44272772296578]
  Large language models (LLMs) have demonstrated their effectiveness in multivariate time series classification. LLMs directly encode embeddings for time series within the latent space of LLMs from scratch to align with semantic space of LLMs. We propose TableTime, which reformulates MTSC as a table understanding task.
  arXiv  Detail & Related papers  (2024-11-24T07:02:32Z)
• Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification [4.5939667818289385]
  HiTime is a hierarchical multi-modal model that seamlessly integrates temporal information into large language models. Our findings highlight the potential of integrating temporal features into LLMs, paving the way for advanced time series analysis.
  arXiv  Detail & Related papers  (2024-10-24T12:32:19Z)
• Prompting and Fine-Tuning of Small LLMs for Length-Controllable Telephone Call Summarization [33.67670065326008]
  This paper explores the rapid development of a telephone call summarization system utilizing large language models (LLMs) Our results show that fine-tuned Llama-2-7B-based summarization model performs on-par with GPT-4 in terms of factual accuracy, completeness and conciseness.
  arXiv  Detail & Related papers  (2024-10-24T10:32:10Z)
• Temporally Consistent Factuality Probing for Large Language Models [16.177991267568125]
  We introduce TeCFaP, a novel Temporally Consistent Factuality Probe task. We extend the definitions of existing metrics to represent consistent factuality across temporal dimension. Next, we propose a novel solution CoTSeLF combining multi-task instruction tuning (MT-IT) with consistent-time-sensitive reinforcement learning (CTSRL) to improve temporally consistent factuality in LLMs.
  arXiv  Detail & Related papers  (2024-09-21T08:41:08Z)
• A Practitioner's Guide to Continual Multimodal Pretraining [83.63894495064855]
  Multimodal foundation models serve numerous applications at the intersection of vision and language. To keep models updated, research into continual pretraining mainly explores scenarios with either infrequent, indiscriminate updates on large-scale new data, or frequent, sample-level updates. We introduce FoMo-in-Flux, a continual multimodal pretraining benchmark with realistic compute constraints and practical deployment requirements.
  arXiv  Detail & Related papers  (2024-08-26T17:59:01Z)
• Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RL [57.202733701029594]
  Decision Mamba is a novel multi-grained state space model with a self-evolving policy learning strategy. To mitigate the overfitting issue on noisy trajectories, a self-evolving policy is proposed by using progressive regularization. The policy evolves by using its own past knowledge to refine the suboptimal actions, thus enhancing its robustness on noisy demonstrations.
  arXiv  Detail & Related papers  (2024-06-08T10:12:00Z)
• Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding [57.62275091656578]
  We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE) This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE.
  arXiv  Detail & Related papers  (2024-06-04T16:42:17Z)
• Scalable Language Model with Generalized Continual Learning [58.700439919096155]
  The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
  arXiv  Detail & Related papers  (2024-04-11T04:22:15Z)
• Empowering Time Series Analysis with Large Language Models: A Survey [24.202539098675953]
  We provide a systematic overview of methods that leverage large language models for time series analysis. Specifically, we first state the challenges and motivations of applying language models in the context of time series. Next, we categorize existing methods into different groups (i.e., direct query, tokenization, prompt design, fine-tune, and model integration) and highlight the key ideas within each group.
  arXiv  Detail & Related papers  (2024-02-05T16:46:35Z)
• Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion [24.545917737620197]
  Temporal Knowledge Graph Completion (TKGC) is a complex task involving the prediction of missing event links at future timestamps. This paper aims to provide a comprehensive perspective on harnessing the advantages of Large Language Models for reasoning in temporal knowledge graphs.
  arXiv  Detail & Related papers  (2024-01-11T17:42:47Z)
• MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning [63.80739044622555]
  We introduce MuSR, a dataset for evaluating language models on soft reasoning tasks specified in a natural language narrative. This dataset has two crucial features. First, it is created through a novel neurosymbolic synthetic-to-natural generation algorithm. Second, our dataset instances are free text narratives corresponding to real-world domains of reasoning.
  arXiv  Detail & Related papers  (2023-10-24T17:59:20Z)

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.