Exploring Long Running News Stories using Wikipedia

Jaspreet Singh, Abhijith Anand, Vinay Setty, Avishek Anand
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引用次数: 1

Abstract

A significant portion of today's news articles are part of long running stories. To better understand the context of these stories journalists, social scientists and other scholars use news collections to find temporal and topical insights. However these insights are devoid of user impressions, derived from click-through data and query logs, and are only reliable if the collection is complete and consistent. In this work we introduce the notion of combining user impressions from Wikipedia with news collection based insights for long running news story exploration and outline promising new research directions. We also demonstrate our initial attempts with a prototype system called NewsEX.
使用维基百科探索长时间运行的新闻故事
今天的新闻文章中有很大一部分是长篇故事的一部分。为了更好地理解这些故事的背景,记者、社会科学家和其他学者使用新闻收集来寻找时间和主题的见解。然而,这些见解缺乏用户印象,来自点击数据和查询日志,只有在收集完整和一致的情况下才可靠。在这项工作中,我们介绍了将来自维基百科的用户印象与基于新闻收集的见解相结合的概念,以进行长期新闻故事的探索,并概述了有前途的新研究方向。我们还用一个名为NewsEX的原型系统演示了我们最初的尝试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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