Timeline Summarization
8 papers with code • 1 benchmarks • 0 datasets
Identifying key dates of major events and providing short descriptions of what happened on these dates. (Source: https://www.aclweb.org/anthology/D19-5403/)
Most implemented papers
A Temporally Sensitive Submodularity Framework for Timeline Summarization
Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates.
Learning towards Abstractive Timeline Summarization
Timeline summarization targets at concisely summarizing the evolution trajectory along the timeline and existing timeline summarization approaches are all based on extractive methods. In this paper, we propose the task of abstractive timeline summarization, which tends to concisely paraphrase the information in the time-stamped events. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, we propose a memory-based timeline summarization model (MTS). Concretely, we propose a time-event memory to establish a timeline, and use the time position of events on this timeline to guide generation process. Besides, in each decoding step, we incorporate event-level information into word-level attention to avoid confusion between events. Extensive experiments are conducted on a large-scale real-world dataset, and the results show that MTS achieves the state-of-the-art performance in terms of both automatic and human evaluations.
Examining the State-of-the-Art in News Timeline Summarization
Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved.
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order
iriscxy/unified-timeline-summarizer
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Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline.
DTELS: Towards Dynamic Granularity of Timeline Summarization
The rapid proliferation of online news has posed significant challenges in tracking the continuous development of news topics.
Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance
We propose an approach that employs a large language model (LLM) to summarize news articles according to a specified constraint and cluster them to identify key events to include in a constrained timeline.
Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization
In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging.
