Examining the State-of-the-Art in News Timeline Summarization
TL
TL;DR
This paper compares different TLS strategies using appropriate evaluation frameworks, and proposes a simple and effective combination of methods that improves over the stateof-the-art on all tested benchmarks.
Abstract
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. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task. In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the stateof-the-art on all tested benchmarks. For a more robust evaluation, we also present a new TLS dataset, which is larger and spans longer time periods than previous datasets.
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A Temporally Sensitive Submodularity Framework for Timeline Summarization
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Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization
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Improving ROUGE for Timeline Summarization
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