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A text summary-based method to detect new events from streams of online news articles
Information & Management ( IF 9.9 ) Pub Date : 2022-07-08 , DOI: 10.1016/j.im.2022.103684
Yen-Hsien Lee , Chih-Ping Wei , Paul Jen-Hwa Hu , Pao-Feng Wu , How Jiang

New event detection (NED), which is crucial to firms’ environmental surveillance, requires timely access to and effective analysis of live streams of news articles from various online sources. These news articles, available in unprecedent frequency and quantity, are difficult to sift through manually. Most of existing techniques for NED are full-text-based; typically, they perform full-text analysis to measure the similarity between a new article and previous articles. This full-text-based approach is potentially ineffective, because a news article often contains sentences that are less relevant to define the focal event being reported and the inclusion of these less relevant sentences into the similarity estimation can impair the effectiveness of NED. To address the limitation of the full-text-based approach and support NED more effectively and efficiently, this study proposes and develops a summary-based event detection method that first selects relevant sentences of each article as a summary, then uses the resulting summaries to detect new events. We empirically evaluate our proposed method in comparison with some prevalent full-text-based techniques, including a vector space model and two deep-learning-based models. Our evaluation results confirm that the proposed method provides greater utilities for detecting new events from online news articles. This study demonstrates the value and feasibility of the text summarization approach for generating news article summaries for detecting new events from live streams of online news articles, proposes a new method more effective and efficient than the benchmark techniques, and contributes to NED research in several important ways.



中文翻译:

一种基于文本摘要的在线新闻文章流中新事件检测方法

新事件检测 (NED) 对公司的环境监测至关重要,需要及时访问和有效分析来自各种在线资源的新闻文章的实时流。这些新闻文章以前所未有的频率和数量提供,很难手动筛选。NED 的大多数现有技术都是基于全文的;通常,他们执行全文分析来衡量新文章和以前文章之间的相似性。这种基于全文的方法可能无效,因为新闻文章通常包含与定义所报道的焦点事件不太相关的句子,并且将这些不太相关的句子包含在相似性估计中会损害 NED 的有效性。为了解决基于全文的方法的局限性并更有效地支持NED,本研究提出并开发了一种基于摘要的事件检测方法,该方法首先选择每篇文章的相关句子作为摘要,然后使用得到的摘要进行检测新事件。与一些流行的基于全文的技术(包括向量空间模型和两个基于深度学习的模型)相比,我们凭经验评估了我们提出的方法。我们的评估结果证实,所提出的方法为检测在线新闻文章中的新事件提供了更大的效用。这项研究证明了文本摘要方法用于生成新闻文章摘要以从在线新闻文章的实时流中检测新事件的价值和可行性,

更新日期:2022-07-08
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