Timeline generation with social attention
Timeline generation with social attention
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DOI：
10.1145/2484028.2484103
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Wayne Xin Zhao;Yanwei Guo;Rui Yan;Yulan He;Xiaoming Li
中科院分区：
文献类型：
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作者：
Wayne Xin Zhao;Yanwei Guo;Rui Yan;Yulan He;Xiaoming Li
时间轴生成是一项重要的研究任务，它可以帮助用户快速了解任何给定主题的整体演变。因此，近年来引起了研究界的广泛关注。然而，现有的时间轴生成的工作往往忽略了一个重要的因素，吸引到感兴趣的话题的注意力（以下称为“社会关注”）。在不考虑社会关注的情况下，生成的时间表可能无法反映用户的集体兴趣。在本文中，我们研究如何将社会关注的生成时间轴摘要。特别是，对于一个给定的主题，我们捕捉社会关注的学习用户的集体利益的形式，从Twitter的单词分布，随后被纳入一个统一的框架，时间轴摘要生成。我们在六个不同的主题上构建了四个评价集。我们证明，我们提出的方法是能够产生翔实和有趣的时间表。我们的工作揭示了将社会注意力纳入传统文本挖掘任务的可行性。
Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.