新聞是生活中不可或缺的知識與資訊來源，電子化使得新聞資訊量的暴增，在如此大量的資訊與知識中，使用者常常會迷失其所想要獲取的知識，而且，我們認為時間性與相關性的新聞檢索不能完全滿足使用者需求，於是我們想要提供使用者更有效的搜尋機制，以幫助使用者更方便找到所想要的資訊。再者，我們觀察到部落格資訊的興起，這些資訊似乎有助於新聞文件的檢索，我們便想藉由部落格的豐富的Web users資訊，來幫助新聞檢索。

於是，我們建立一個HOT Event Extraction機制的搜尋系統，主要分成四大部分，第一部分為利用時間區段來使搜尋結果集中。第二部分我們找出相關的Terms當成是尋找HOT Events的引子，第三部分我們利用定義的Novelty、Bursty和Popularity這三個因素所形成的HOT Event Extraction機制來找出HOT Events的，第四部分為Application，我們引入Clarity的機制來達成Sentence Selection and Title Generation，主要在於將我們所選出來的HOT Events做一個資訊簡化的動作。

系統結合了新聞與部落格這兩大資訊來源，利用這來兩大資訊來源以及HOT Event Extraction的機制，讓使用者可以找到其所想要的相關資訊。另外我們額外作了Sentence Selection和Title Generation精簡的動作，主要是想讓使用者能更一目了然，知道所發生的事件。最後，我們利用實驗結果來驗證HOT Event Extraction機制的效用，其中HOT Event Extraction機制會比一些基本的方法像是TF、TFIDF與Ordering by Time的方法好，然後我們並討論使用者認知上的差異對實際HOT Event Extraction機制的結果所產生的影響。

News is the indispensable source of knowledge and information in life. Online news information has increased substantially. In such a huge amount of information and knowledge, users often lose his way to acquire knowledge. Furthermore, we want to provide users with other search mechanism outside the timing and relevance to help users more convenient in finding the desired information. In addition, we observed that the rise of blog information and such information seems to contribute to news search. In this thesis, we intend to use the very huge of blog information to enhance new search.

Thus, we try to develop a news search system with mechanism of HOT Event Extraction. The system are mainly divided into four parts. The first part, we use time interval to constrain relevance region of hot events. The second part, we find hot terms to extract hot events. The third part, we defined and use the three factors: Novelty, Bursty and Popularity to make our HOT Event Extraction mechanism effective to find hot events. The part IV is Application. We use Clarity mechanism to effectively deal with Sentence Selection and Title Generation. This Part is mainly making hot events more simplified.

Our news search system integrated two information sources, news and blog and use the HOT Event Extraction mechanism to make users find their desired information quickly. Also, we designed additional mechanisms of Sentence Selection and Title Generation to make it easier for users to efficiently understand hot events. Finally, the experimental showed results to validate the effectiveness of HOT Event Extraction mechanism. According to experimental results, we can find HOT Event Extraction mechanisms perform better than the baseline method such as TF, TFIDF and Ordering by Time. Then we discussed the impact of users with different perception for the result of HOT Event Extraction.

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