提出一種使用段落自動(dòng)聚類(lèi)思想的自動(dòng)文摘方法，首先利用詞頻統(tǒng)計(jì)和詞的位置特征得到文檔的關(guān)鍵詞向量、每個(gè)段落的關(guān)鍵詞向量，并建立以段落為基礎(chǔ)的向量空間模型；然后計(jì)算各段落間的相似度，采用K-medoids 聚類(lèi)算法實(shí)現(xiàn)文檔語(yǔ)義段的劃分,并通過(guò)一個(gè)自定義的目標(biāo)函數(shù)來(lái)自適應(yīng)的確定聚類(lèi)數(shù)目K；最后根據(jù)在初始文檔中的位置順序從各語(yǔ)義段中選出與主題最相關(guān)的句子構(gòu)成文摘。
關(guān)鍵詞：自動(dòng)文摘 語(yǔ)義段劃分 向量空間模型 聚類(lèi) K-medoids

Study on Adaptive Clustering of Paragraphs in Automatic Summarization System Liu Haitao Lao Songyang Han Zhiguang (Department of Information System and Management, National University of Defence Technology, Changsha 410073) Abstract： Presents a useful automatic summarization method that uses automatic clustering thought. Firstly, the keyword vectors of a document and that of each paragraph of the document are got according to word frequency statistic and position feature. Based on paragraph, the vector space model for the whole article is established. Secondly, the similarity degree between paragraphs is calculated. The paragraphs of the document are classified into semantic paragraph by K-medoids clustering methods. K, the number of clusters, is determined by a self-defined objective function. Finally, according to their positions in the original document, the representative sentences are selected from each semantic paragraph to form the final summarization.
Keyword：Automatic summarization; Semantic Paragraph Partition; Vector Space Model; Clustering; K-medoids