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Fractal Summarization

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Fu Lee Wang
added 9 research items
Wireless access with mobile devices is a promising addition to the WWW and traditional electronic business. Mobile devices provide convenience and portable access to the huge information space on the Internet. It is desire to access the most updated financial information through mobile devices in order to make critical and urgent decision for most of the investors. In this paper, we present a financial news delivery system on mobile devices based on the fractal summarization model. Fractal summarization is developed based on the fractal theory. It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of the document are generated on demands of users. Such interactive summarization reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional summarization, which is ideal for wireless access.
Wireless access with mobile (or handheld) devices is a promising addition to the WWW and traditional electronic business. Mobile devices provide convenience and portable access to the huge information space on the Internet without requiring users to be stationary with network connection. However, the limited screen size, narrow network bandwidth, small memory capacity and low computing power are the shortcomings of handheld devices. Loading and visualizing large documents on handheld devices become impossible. The limited resolution restricts the amount of information to be displayed. The download time is intolerably long. In this paper, we introduce the fractal summarization model for document summarization on handheld devices. Fractal summarization is developed based on the fractal theory. It generates a brief skeleton of summary at the first stage, and the details of the summary on different levels of the document are generated on demands of users. Such interactive summarization reduces the computation load in comparing with the generation of the entire summary in one batch by the traditional automatic summarization, which is ideal for wireless access. Three-tier architecture with the middle-tier conducting the major computation is also discussed. Visualization of summary on handheld devices is also investigated.
Fu Lee Wang
added 6 research items
As a result of the rapid growth in Internet access, significantly more information has become available online in real time. However, there is not sufficient time for users to read large volumes of information and make decisions accordingly. The problem of information-overloading can be resolved through the application of automatic summarization. Many summarization systems for documents in different languages have been implemented. However, the performance of summarization system on documents in different languages has not yet been investigated. In this paper, we compare the result of fractal summarization technique on parallel documents in Chinese and English. The grammatical and lexical differences between Chinese and English have significant effect on the summarization processes. Their impact on the performances of the summarization for the Chinese and English parallel documents is compared.
As a result of the recent information explosion, there is an increasing demand for automatic summarization, and human abstractors often synthesize summaries that are based on sentences that have been extracted by machine. However, the quality of machine-generated summaries is not high. As a special application of information retrieval systems, the precision of automatic summarization can be improved by user relevance feedback, in which the human abstractor can direct the sentence extraction process and useful information can be retrieved efficiently. Automatic summarization with relevance feedback is a helpful tool to assist professional abstractors in generating summaries, and in this work we propose a relevance feedback model for fractal summarization. The results of the experiment show that relevance feedback effectively improves the performance of automatic fractal summarization.