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In order to over the shortcoming of the incomprehensive of summarization, a new lexical-chain-based keywords extraction and automatic summarization algorithm from Chinese texts based on the unknown word recognition using co-occurrence of neighbor words is proposed in this paper, and an algorithm for constructing
of text summarization is accurate identification of keywords from the given textual content. In this paper, the relative performance of three popular algorithms, namely TextRank, LexRank and Latent Semantic Analysis for keyword extraction were investigated by measuring their effectiveness in identifying keywords from
Internet is becoming an increasingly important platform for ordinary life and work. It is expected that keyword extraction can help people quickly find hot spots on the web, since keywords in a document provide important information about the content of the document. In this paper, we propose to use text clustering
Keywords can be considered as condensed versions of documents, which can play important role in some text processing tasks such as text indexing, summarization and categorization. However, there are many digital documents especially on the Internet that do not have a list of assigned keywords. Assigning keywords to
Text keywords at different semantic levels have different semantic representation abilities. Although words have been organized by semantic dictionaries (e.g. WordNet) with exact semantics, the dictionaries can not be constructed automatically by machine and there are still many words which are not included in the
In this research, we used a proxy server to search for information related to the userpsilas browsed Web pages. From the records of the proxy server we constructed a profile of the userpsilas browsing habits. At the end of the userpsilas search subsystem, we will use content based concept to extract keywords to obtain
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