With information explosion on the Internet, only returning ranked documents by search engines cannot satisfy people’s requirements on news events understanding. A more intelligent news events search engine should not only retrieve all related documents about a specific event, but also provide a global view about how the event originates and evolves. In order to solve this challenge, two tasks, event news retrieval and eventline generation should be processed. For event news retrieval, existing approaches mainly focus on the document-level similarity to retrieve related news documents, while external knowledge is not effectively taken into consideration. To this end, we propose a similarity model named Event-Oriented Similarity combining the document-level with the knowledge-level similarity to retrieve news documents related to the specific event. For eventline generation, in order to outline the event structure more accurately, we construct an Event-Oriented Similarity Graph to represent the relationship among retrieved event news documents and develop a community detection algorithm to segment sub-events which are consequently chained into a cohesive eventline. Experimental results on real-world datasets demonstrate that the proposed approach outperforms existing methods.

中文翻译：

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了解新闻事件演变的另一种方法

随着Internet上信息的爆炸式增长，仅搜索引擎返回的排名文档不能满足人们对新闻事件理解的要求。更加智能的新闻事件搜索引擎不仅应检索有关特定事件的所有相关文档，还应提供有关事件如何起源和演变的全局视图。为了解决这一挑战，应处理两项任务，即事件新闻检索和事件线生成。对于事件新闻检索，现有方法主要集中在文档级别的相似性来检索相关新闻文档，而没有有效地考虑外部知识。为此，我们提出了一个名为“面向事件的相似度”的相似度模型，将文档级和知识级的相似度相结合，以检索与特定事件相关的新闻文档。对于事件线生成，为了更准确地勾勒事件结构，我们构造了一个面向事件的相似度图来表示检索到的事件新闻文档之间的关系，并开发了一种社区检测算法来细分子事件，从而将这些子事件链接到一个内聚的事件线中。在真实数据集上的实验结果表明，该方法优于现有方法。