伴隨網際網路時代的來臨，資訊量成指數性的爆炸成長，資訊過載已成為急需解決的問題。如何從龐大的資料來源中，快速準確地擷取出符合使用者需求的資訊，誠乃一門重要的學問。在本領域中，文件摘要技術更是扮演關鍵的角色。摘要技術的作用在於將含有雜亂資訊的龐大文件集，以精短簡潔的文句段落，來表達文件集中重要的關鍵資訊，有助使用者節省閱讀的時間與精力。欲從一龐大文件集中產生摘要，必須分析整份文件集，對各篇文件所含的資訊加以萃取過濾，並予以整理合併，才能摘要出精簡的文句段落。現行的文件自動摘要技術，多以統計分析的方式，擷取文件中具代表性的關鍵文句組合成摘要，或是以計算相似度的方式，萃取出文件集的代表性概念，在將其擴充組合為文句段落。不論在可閱讀性跟連貫性方面，都有改善空間。本研究建構一個以本體論(ontology)為基礎的英文文件集摘要產生系統，將摘要知識結構以本體論來表達。首先將文件集做一初步的分群，有助於接下來摘要產生的效率與正確性，接著計算各文句的特徵值，並將其與以加權加總，以計算出各文句在此多文件群集中的重要性，再將此排序，得知各文句的重要性順序，以摘錄出真正重要的文句。接下來，對摘錄出的文句做文件前處理，包含斷句跟詞性標記的動作，以輔助下一個步驟的處理。再來，便是最終摘要修飾的部份。本研究提出對最終摘要文句加以進一步註解的方式，方法是以領域本體論和WordNet語彙典為輔，計算文句之間的相似度，以得出文句間彼此的關連程度。最後，若文句間相關性超過預設的門檻值，則予以做進一步的註解，並且建立文句之間的超連結，讓最終摘要的文句間關聯得以彰顯與明確表達，有助於使用者閱讀並且掌握該份多文件摘要的資訊。本研究的多文件集實驗資料為ACM組織(Association for Computing Machinery)下的SIGIR(Special Interest Group on Information Retrieval)研討會中，所發表關於文件摘要領域的論文摘要。本研究在英文多文件集摘要處理上，最大的最終摘要效用品質百分比可達到將近80%的水準，並能提升使用者閱讀時的便利性。

With the coming of Internet, the amount of information has been grown exponentially. As a result, information overloading has become a severe problem. How to retrieval information suitable for users from great numbers of sources correctly and efficiently is indeed import courses, and the techniques of documents summarization play a great roles to this problem. They are applied to retrieve salient sentences from mass documents corpus to represent the most important information for users to save time and energy on reading and filtering. However, to produce coherent and irredundant summarization from huge documents corpus, we have to analyze the whole corpus and then refine, retrieve, filter, merge, and order information contained in each documents.
The most of existing techniques of documents summarization adopt statistical methodologies to extract the salient sentences to compose the final summarization. Otherwise, some use the calculation of similarity to retrieve the representative concept within corpus and then expand and combine them to form a sentences or paragraph. They urgently have to be improved no matter on readability or coherence. Consequently, the research implements an ontology-based English multi-documents summarization system. First, an initial clustering is made on our corpus to improve the efficiency and accuracy of the summarization generation. Then to measure the importance of each sentence in the document corpus, the feature values of each sentence will be calculated and be summed up after multiplying their own weights. We will rank the sentences according to their total feature values to acquire the order of their importance. With the ranked order, the real salient sentences can be easily and accurately extracted. In the next step, we will preprocess the extracted sentences by sentence segmentation and POS tagging to assist the processing of the followed step, which is the surface generation. The methodology of advanced tagging on the extracted sentences in the final summarization is proposed, which is assisted with the specified domain ontology and the thesaurus called WordNet to calculate the similarity between two sentences and acquire the degree of their association. Finally, if the similarity is bigger than the pre-defined threshold, the advanced tagging will be attached and the URL connecting to its similar sentences will be automatically constructed. It helps the sentences in the final summarization express their association between each other more understandable and clear. For users, the final summarization is more readable and absorbable. The corpuses for our use come from the conference called SIGIR (Special Interest Group on Information Retrieval) of ACM (Association for Computing Machinery). All of them are the abstracts of the theses in the research field of text summarization. An English multi-documents summarization system is implemented by the research, and to improve the readability and coherence of the final summarization, the maximum utility-based performance can approach nearly 80 percent.

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