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篇名
Multiple Document Summarization Using Principal Component Analysis Incorporating Semantic Vector Space Model
作者 Om Vikas (Om Vikas)Akhil K Meshram (Akhil K Meshram)Girraj Meena (Girraj Meena)Amit Gupta (Amit Gupta)
中文摘要
Text Summarization is very effective in relevant assessment tasks. The Multiple Document Summarizer presents a novel approach to select sentences from documents according to several heuristic features. Summaries are generated modeling the set of documents as Semantic Vector Space Model (SVSM) and applying Principal Component Analysis (PCA) to extract topic features. Pure Statistical VSM assumes terms to be independent of each other and may result in inconsistent results. Vector space is enhanced semantically by modifying the weight of the word vector governed by Appearance and Disappearance (Action class) words. The knowledge base for Action words is maintained by classifying the words as Appearance or Disappearance with the help of Wordnet. The weights of the action words are modified in accordance with the Object list prepared by the collection of nouns corresponding to the action words. Summary thus generated provides more informative content as semantics of natural language has been taken into consideration.
起訖頁 141-156
關鍵詞 Principal Component Analysis (PCA)Semantic Vector Space Model (SVSM)SummarizationTopic FeatureWordnet
刊名 中文計算語言學期刊  
期數 200806 (13:2期)
出版單位 中華民國計算語言學學會
該期刊-下一篇 A Study on Consistency Checking Method of Part-Of-Speech Tagging for Chinese Corpora1
 

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