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ÇѱÛÁ¦¸ñ(Korean Title) |
µ¿Àû ¿¬°á ±×·¡ÇÁ¸¦ ÀÌ¿ëÇÑ ÀÚµ¿ ¹®¼ ¿ä¾à ½Ã½ºÅÛ |
¿µ¹®Á¦¸ñ(English Title) |
A Document Summarization System Using Dynamic Connection Graph |
ÀúÀÚ(Author) |
¼Û¿ø¹®
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±èÀºÁÖ
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WonMoon Song
YoungJin Kim
EunJu Kim
MyungWon Kim
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 36 NO. 01 PP. 0062 ~ 0069 (2009. 01) |
Çѱ۳»¿ë (Korean Abstract) |
¹®¼ ¿ä¾àÀº ½±°í ºü¸£°Ô ¹®¼ÀÇ ³»¿ëÀ» ÆľÇÇÒ ¼ö ÀÖµµ·Ï ¹æ´ëÇÑ ³»¿ëÀ» °¡Áö´Â ´Ù¾çÇÑ ÇüÅÂÀÇ ¹®¼·ÎºÎÅÍ ÇÙ½É ³»¿ë¸¸À» ÃßÃâÇϰųª »ý¼ºÇÏ¿© Á¦°øÇÏ´Â °ÍÀ» ¸ñÀûÀ¸·Î ÇÑ´Ù. º» ³í¹®¿¡¼´Â È¿À²Àû ¹®¼ ¿ä¾àÀ» À§ÇØ ÁÖ¾îÁø ¹®¼ÀÇ Æò±Õ ¹®Àå ±æÀÌ(ÇÙ½É¾î °³¼ö)¸¦ °í·ÁÇÏ¿© ¹®Àå °£ÀÇ Çٽɾî À¯»çµµ¸¦ ³ªÅ¸³»´Â ¿¬°á ±×·¡ÇÁ¸¦ »ý¼ºÇÏ°í ºÐ¼®ÇÏ¿© ¿ä¾àÀ» »ý¼ºÇÏ´Â ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. ¶ÇÇÑ ÀÌ·¯ÇÑ ±â¹ýÀ» ÀÌ¿ëÇÏ¿© ÀÀ¿ë ÇÁ·Î±×·¥ ¹®¼·ÎºÎÅÍ ÀÚµ¿À¸·Î ¿ä¾àÀ» »ý¼ºÇÏ´Â ÀÚµ¿ ¹®¼ ¿ä¾à ½Ã½ºÅÛÀ» °³¹ßÇÑ´Ù. Á¦¾ÈÇÑ ¹æ¹ýÀÇ °´°üÀûÀÎ ¿ä¾à ¼º´É ÃøÁ¤À» À§ÇØ Á¤È®ÇÑ ¿ä¾à¹®ÀÌ ½Ç¸° 20°³ÀÇ Å×½ºÆ® ¹®¼¸¦ ÀÌ¿ëÇÏ¿© »ý¼ºµÈ ¿ä¾à¿¡ ´ëÇØ precision(Á¤È®·ü)°ú recall(ÀçÇöÀ²), F-measure¸¦ ÃøÁ¤ÇÏ¿´À¸¸ç, ½ÇÇè °á°ú¸¦ ÅëÇØ ±âÁ¸ ±â¹ý¿¡ ºñÇØ ¿ì¼öÇÑ ¿ä¾à ¼º´ÉÀ» º¸ÀÓÀ» Áõ¸íÇÏ¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
The purpose of document summarization is to provide easy and quick understanding of documents by extracting summarized information from the documents produced by various application programs. In this paper, we propose a document summarization method that creates and analyzes a connection graph representing the similarity of keyword lists of sentences in a document taking into account the mean length(the number of keywords) of sentences of the document. We implemented a system that automatically generate a summary from a document using the proposed method. To evaluate the performance of the method, we used a set of 20 documents associated with their correct summaries and measured the precision, the recall and the F-measure. The experiment results show that the proposed method is more efficient compared with the existing methods. |
Å°¿öµå(Keyword) |
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Áß¿ä¹®Àå ÃßÃâ
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Document Summarization
Dynamic Connection Graph
Extractive Summarization
Keysentences Extraction
Keywords Similarity
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