隨著網路的發達和興盛，網路上的資料量也相對日益倍增。使用者必須花費大量時間才能過濾或是搜尋到想要的資訊。因此，藉由自動化摘要技術的發展，可以自動的從大量的文件或網頁中擷取出重要的主旨和概念以方便讀者閱覽。目前自動化摘要朝著兩個方向發展，其中一個是利用查詢句來搜尋讀者有興趣的摘要內容，另一種則是從原始文件中直接摘要出主要的概念和主旨。前者是屬於資訊檢索的領域範圍稱為關鍵字摘要，後者則是屬於傳統的文件摘要。
在本論文中，我們提出架構於LDA(latent Dirichlet allocation)方法的自動化摘要技術，是以模型訓練為主的方法，有別於傳統習慣採用空間向量的表示法。以模型訓練為主的摘要技術可以避免未出現的字無法估測權重問題，並且可利用同義詞或一起出現的詞共同分享相似的機率值以提高真正重要的句子被擷取出來成為摘要文句的機率。此外，根據LDA本身已經有的最大優點就是當有新文件時，可以不用重新訓練，即可利用原本的模型參數直接推估新文件的機率模型。
一旦模型的參數得知後，本論文的方法不僅可以用於文字摘要的領域上，還可以進階延伸到資訊擷取領域中的關鍵字摘要。從實驗結果也顯示，本論文所提的架構的確夠有比較好的摘要效能。

As the internet grows speedily and prosperously, the amount of information is too large and too miscellaneous to browse. Users have to spend a lot of time to dig out the information they need. The automatic text summarization was accordingly developed to help extracting the concepts or themes among large documents or web pages. There are two types of automatic summarization. One of them is to extract the theme from user’s query, while another is to find out important notes from original articles directly. The former is applied in the domain of information retrieval, and the latter is used for text summarization.
In this thesis, we addressed a new automatic summarization technique based on the state-of-art latent Dirichlet allocation (LDA) model and applied it for text summarization. Different from the traditional vector space summarization methods, we adopted the sentence-based LDA (SLDA) model to derive the summary of a document. This SLDA method can tackle the problem of unseen words by sharing the information from synonyms and co-occurrence words and extracting the true critical sentences from given articles. Furthermore, this SLDA method can be easily generalized to new documents and calculated for sentence selection without model retraining. Using the trained model parameters, the proposed method is not only available for text summarization, but also extendible to query summarization. The experiment results are shown better performance than other methods.

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