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Based on the theme by Chinese single - document summarization system

Author: ZhangYuanHong
Tutor: GuoJianYi
School: Kunming University of Science and Technology
Course: Applied Computer Technology
Keywords: Automatic Summarization Topic Partition Abstract generation Summary optimization
CLC: TP391.1
Type: Master's thesis
Year: 2009
Downloads: 47
Quote: 0
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Abstract


Automatic summarization is an important application as the field of natural language processing , but also a very difficult and challenging task , has been widely applied in the field of information retrieval, information management, digital libraries . Therefore, the study of automatic summarization has great theoretical and practical significance . Statistics - based automatic summarization is a study earlier abstracts and widely used method . A major advantage of this approach is the field is not restricted, different articles in the field can use this method to digest . But this approach abstracts and there is not comprehensive, concise and coherent three shortcomings , making the summary of the results is not the best of man . In this paper, automatic summarization of statistical method based on topic segmentation and summary sentences based on statistical automatic summarization method , so that the generated summary of a more comprehensive , concise , consistent technology integrated into the optimization of two parts . The content of this study include the following aspects: ( 1 ) using a modified K-means algorithm is proposed to divide the theme of the text , extracted more comprehensive summary sentence . 2 to optimize the processing of crude summary sentence in the the generated rough summary sentence based on the output summary sentence more concise and coherent . On the basis of the above two steps , the development of a Chinese single-document automatic summarization prototype system . The system , the use of internal evaluation tools available to assess the performance of the system , including the \Word2003 automatic summarization system.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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