隨著網際網路的發展，網路上資訊所包含的範圍也越來越廣，資訊量也比以往更為豐富。而大量的資訊卻造成資訊過載的問題，使人們無法去處理這些大量的資訊。過度的資訊充斥在網路上，迫使人們必須花大量的時間在審視資訊的好壞及符不符合需求，並逐一進行資訊的篩選。當然這些問題可以透過搜尋引擎得到不小程度的改善，但對使用者而言，得逐一檢視每一筆資訊依舊是不變的事實。為有效解決資訊過載的問題，各式資訊擷取的技術紛紛被提出，文件摘要自動化(Automatic Summarization)的概念也應運而生。透過文件摘要自動化的模式，抽出文中重要的文句，讓人們能快速理解文中的內容，減輕資訊過載的問題。
　　今日文件摘要自動化的方法有很多種，其中以本體論模式進行摘要自動化為其中一種較好的方式。然而，本體論的建構往往需耗費大量人力在建立及維護之上，反造成本體論摘要模式在應用上的困難度。因此為增進實用性，自動本體論建構是首要工作。但完成本體論建構後，傳統的本體論摘要模式在摘要句的擷取上僅以是否與本體論概念相似為判斷依據，卻未考量到「理解程度」、「主題相關」這兩大特性。由於上述兩點原因，而使文件摘要自動化的技術目前多僅應用在短篇的文獻之中。
　　有鑑於此，本研究提出一套全自動的本體論摘要模式，先以概念擷取的模式自動建立本體論，並以此本體論為摘要句判斷的依據。此外更透過Position和Topic Detection的技術進一步改善「理解程度」及「主題相關」的問題，以提升摘要結果的品質。

　　Due to Internet became popular, the information on Internet is growing fast and the content is very diverse. This situation leads to the problem of information overload which people cannot find the required information easily. It forces human to consume much time on information filtering to find useful information. This problem can be lightened by search engine, but user still has to check each searching result one by one. To efficiently solve this problem, many studies have been proposed by information retrieval. Automatic text summarization is one solution for reducing the problem of information overload. This technique can extract important sentences to represent the subject of information, and let human understand information as soon as possible.
　　There are many approaches to do this job, and one is the ontology-based summarization approach. However, the ontology construction and maintenance always need many human efforts. These causes the ontology-based approach hardly apply in the real world. To enhance the practicability of this approach, we should consider how to construct ontology automatically. In addition, we also need to think about “Reading Comprehension” and “Topic relevance”.
　　According to these problems mentioned above, this thesis proposed one ontology-based summarization approach which is full-automatic on the ontology construction and maintenance. In addition, the summarizer of this thesis not only uses the ontology to generate summaries, but also considers the “Reading Comprehension” and “Topic relevance” features.

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網站資料

HowNet
(http://www.keenage.com/)
Jena Semantic Web Framework
(http://jena.sourceforge.net/)
JWNL Overview
(http://jwordnet.sourceforge.net/)
Standard Upper Ontology Working Group
(http://suo.ieee.org/)
The Stanford Natural Language Processing Group
(http://nlp.stanford.edu/software/lex-parser.shtml)
Thesaurus.com
(http://thesaurus.reference.com/)
WordNet
(http://wordnet.princeton.edu/)