| 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. |
Similar Papers
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. |
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. |
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 . |
DTELS: Towards Dynamic Granularity of Timeline Summarization (2025.naacl-long)
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| Challenge: | Existing timeline summarizations lack flexibility to meet diverse granularity needs . a fine-grained timeline showing the technical details is preferred for news topics . |
| Approach: | They propose a new paradigm to construct adaptive timelines based on user instructions or requirements. |
| Outcome: | The proposed timelines are informative and granularly consistent, but they struggle to generate consistent timelines. |
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. |
SummEval: Re-evaluating Summarization Evaluation (2021.tacl-1)
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Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | a lack of comprehensive studies on evaluation metrics for text summarization hinders progress . a new study aims to improve evaluation metrics that correlate with human judgments . |
| Approach: | They propose to re-evaluate automatic evaluation metrics and share a toolkit for evaluation . they hope to promote a more complete evaluation protocol for text summarization . |
| Outcome: | The proposed evaluation metrics are inconsistent with existing evaluation protocols. |
Background Summarization of Event Timelines (2023.emnlp-main)
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| Challenge: | Generating concise summaries of news events is a challenging task for newcomers to a news story. |
| Approach: | They propose a task of background news summarization that complements each timeline update with a background summary of relevant preceding events. |
| Outcome: | The proposed system performs well on a question-answering-based evaluation metric, Background Utility Score (BUS). |
Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport (2021.emnlp-main)
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| Challenge: | Existing methods for timeline summarization ignore the events’ intra-structures and inter-structure connections. |
| Approach: | They propose to represent news articles as an event-graph, thus compressing the whole graph to its salient sub-graph. |
| Outcome: | The proposed method significantly improves on the state-of-the-art on three real-world datasets, including two public benchmarks and a Timeline100 dataset. |
A Critical Look at Meta-evaluating Summarisation Evaluation Metrics (2024.findings-emnlp)
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| Challenge: | Effective summarisation evaluation metrics enable researchers and practitioners to compare different summarization systems efficiently. |
| Approach: | They argue that evaluation metrics are primarily meta-evaluated on news summarisation datasets and that there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
| Outcome: | The evaluation metrics are primarily meta-evaluated on news summarisation datasets and there has been a noticeable shift in research focus towards evaluating the faithfulness of generated summaries. |
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation (2021.acl-short)
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| Challenge: | Existing summarization datasets are limited in their ability to evaluate output . a human evaluation is necessary to understand and improve summarizing systems . |
| Approach: | They propose a dataset based on how-to articles and coherent paragraph summaries written in plain language. |
| Outcome: | The proposed dataset makes human evaluation easier and more effective . the authors compare the proposed dataset to existing ones on PubMed and the literature. |