Papers with TLS
Abstractive Timeline Summarization (D19-54)
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| Challenge: | Prior approaches to TLS focus on extractive methods, which generate extractive timelines . a study with human judges shows that our abstractive system also produces output that is easy to read and understand. |
| Approach: | They propose an abstractive timeline summarization system that is unsupervised . their system outperforms extractive systems in terms of ROUGE scores . |
| Outcome: | The proposed system outperforms extractive systems in terms of ROUGE scores . it produces output that is easy to read and understand, the authors say . |
Multi-TimeLine Summarization (MTLS): Improving Timeline Summarization by Generating Multiple Summaries (2021.acl-long)
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| Challenge: | Existing work on Time-Line Summarization (TLS) has focused on improving the performance of summarization but its drawbacks are as follows: a homogeneous dataset makes it hard to generalize; output is usually a single timeline regardless of the size and complexity of the input dataset. |
| Approach: | They propose a task that generates a time-line for each story given a news article . they propose MTLS task that can be generalized to other news articles . |
| Outcome: | The proposed task can generate bet-ter results than Time-Line Summarization (TLS) the proposed task is based on previous evaluation methods. |
R2A-TLS: Reflective Retrieval-Augmented Timeline Summarization with Causal-Semantic Integration (2025.findings-emnlp)
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| Challenge: | Existing methods struggle to capture coherent event narratives due to fragmented descriptions . Existing approaches accumulate noise through iterative retrieval strategies that lack relevance evaluation. |
| Approach: | They propose a reflective retrieval-augmented timeline summarization with Causal-Semantic Intergration approach for open-domain timeline summarizing . |
| Outcome: | The proposed approach outperforms the best prior published approaches. |
Examining the State-of-the-Art in News Timeline Summarization (2020.acl-main)
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| Challenge: | Existing work on news timeline summarization (TLS) has left an unclear picture of how well it is currently solved and how it can be approached. |
| Approach: | They propose a combination of different TLS strategies that improves over the stateof-the-art on all tested benchmarks. |
| Outcome: | The proposed method improves over the state-of-the-art on all tested benchmarks. |
Dataset Reproducibility and IR Methods in Timeline Summarization (2020.lrec-1)
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| Challenge: | Timeline summarization (TLS) generates a dated overview of real-world events based on event-specific corpora. |
| Approach: | They propose to use IR methods to construct event-specific corpora from a newsroom dataset . they advocate for integrating IR into the development of TLS systems . |
| Outcome: | The proposed method is not reproducible at different search times and uses components that are not always available for large news corpus. |
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization (2025.findings-naacl)
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| Challenge: | a new approach to timeline summarization is proposed for open-domain news content . large language models (LLMs) can be used to extract and organize news events from multiple documents . |
| Approach: | They propose a method to integrate Large Language Models into news timeline summarization by iterating on how events are linked and posing new questions. |
| Outcome: | The proposed system is able to generate and refresh chronological summaries based on documents retrieved in each round. |
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization (2022.naacl-main)
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| Challenge: | Existing studies on timeline summarization ignore the information interaction between sentences and dates, and combine them as two separate tasks. |
| Approach: | They propose a joint learning-based heterogeneous graph attention network for timeline summarization (HeterTls) they combine date selection and event detection into a unified framework to improve extraction accuracy . |
| Outcome: | The proposed model outperforms state-of-the-art models on four datasets . it significantly outperformed the baseline models on ROUGE scores and date selection metrics . |
From Moments to Milestones: Incremental Timeline Summarization Leveraging Large Language Models (2024.acl-long)
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| Challenge: | Prior work on timeline summarization has neglected the potential synergy between the two forms of timelines. |
| Approach: | They propose a timeline summarization approach that leverages large language models to generate both event and topic timelines. |
| Outcome: | The proposed approach outperforms the best existing approaches in four TLS benchmarks. |
Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration (2026.acl-long)
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| Challenge: | Large language models (LLMs) improve event synthesis, but most are monolithic and often process overlapping evidence with bursty reporting patterns. |
| Approach: | They propose a multi-agent framework that casts TLS as a *newsroom-like* collaboration. |
| Outcome: | Experiments on three benchmarks show that MAS-TLS improves semantic coverage and temporal grounding while substantially reducing token usage and latency. |