Timeline Summarization from Social Media with Life Cycle Models
Timeline Summarization from Social Media with Life Cycle Models
复制标题
DOI：
--
复制
发表时间：
2016-07
期刊：
影响因子：
--
通讯作者：
Yi Chang;Jiliang Tang;Dawei Yin;M. Yamada;Yan Liu
中科院分区：
文献类型：
--
作者：
Yi Chang;Jiliang Tang;Dawei Yin;M. Yamada;Yan Liu
社交媒体的普及打破了在线用户在任何时间任何地点创建和共享信息的障碍。因此，定位关于实体的相关性信息变得越来越困难。时间轴已被证明通过按时间顺序显示关于实体的剧集列表来提供理解实体的有效且高效的访问。然而，用社交媒体数据总结关于实体的时间轴面临着新的挑战。首先，关于实体的关键时间轴情节通常在现有社交媒体服务中不可用。其次，社交媒体帖子的简短，嘈杂和非正式性质决定了仅基于内容的摘要可能是不够的。本文研究了时间轴摘要问题，提出了一个新的摘要框架Timeline-Sumy，该框架包括情节检测和摘要排序两部分。在事件检测中，我们明确地用生命周期模型来建模时间信息，以检测时间轴事件，因为事件通常在时间序列上表现出突然上升和重尾模式。在摘要排名中，我们通过学习排名方法对每集的社交媒体帖子进行排名。在社交媒体数据集上的实验结果证明了该框架的有效性。
The popularity of social media shatters the barrier for online users to create and share information at any place at any time. As a consequence, it has become increasing difficult to locate relevance information about an entity. Timeline has been proven to provide an effective and efficient access to understand an entity by displaying a list of episodes about the entity in chronological order. However, summarizing the timeline about an entity with social media data faces new challenges. First, key timeline episodes about the entity are typically unavailable in existing social media services. Second, the short, noisy and informal nature of social media posts determines that only content-based summarization could be insufficient. In this paper, we investigate the problem of timeline summarization and propose a novel framework Timeline-Sumy, which consists of episode detecting and summary ranking. In episode detecting, we explicitly model temporal information with life cycle models to detect timeline episodes since episodes usually exhibit sudden-rise-and-heavy-tail patterns on time-series. In summary ranking, we rank social media posts in each episode via a learning-to-rank approach. The experimental results on social media datasets demonstrate the effectiveness of the proposed framework.