Automatic Keyword Extraction for Text Summarization: A Survey <s> Coherent Based Approach <s> In text, lexical cohesion is the result of chains of related words that contribute to the continuity of lexical meaning. These lexical chains are a direct result of units of text being "about the same thing," and finding text structure involves finding units of text that are about the same thing. Hence, computing the chains is useful, since they will have a correspondence to the structure of the text. Determining the structure of text is an essential step in determining the deep meaning of the text. In this paper, a thesaurus is used as the major knowledge base for computing lexical chains. Correspondences between lexical chains and structural elements are shown to exist. Since the lexical chains are computable, and exist in non-domain-specific text, they provide a valuable indicator of text structure. The lexical chains also provide a semantic context for interpreting words, concepts, and sentences. <s> BIB001 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Coherent Based Approach <s> The rapid growth of the Internet has resulted in enormous amounts of information that has become more difficult to access efficiently. Internet users require tools to help manage this vast quantity of information. The primary goal of this research is to create an efficient and effective tool that is able to summarize large documents quickly. This research presents a linear time algorithm for calculating lexical chains which is a method of capturing the “aboutness” of a document. This method is compared to previous, less efficient methods of lexical chain extraction. We also provide alternative methods for extracting and scoring lexical chains. We show that our method provides similar results to previous research, but is substantially more efficient. This efficiency is necessary in Internet search applications where many large documents may need to be summarized at once, and where the response time to the end user is extremely important. <s> BIB002 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Coherent Based Approach <s> WordNet: Similarity is a freely available software package that makes it possible to measure the semantic similarity or relatedness between a pair of concepts (or word senses). It provides six measures of similarity, and three measures of relatedness, all of which are based on the lexical database WordNet. These measures are implemented as Perl modules which take as input two concepts, and return a numeric value that represents the degree to which they are similar or related. <s> BIB003 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Coherent Based Approach <s> In this paper, we introduce and compare between two novel approaches, supervised and unsupervised, for identifying the keywords to be used in extractive summarization of text documents. Both our approaches are based on the graph-based syntactic representation of text and web documents, which enhances the traditional vector-space model by taking into account some structural document features. In the supervised approach, we train classification algorithms on a summarized collection of documents with the purpose of inducing a keyword identification model. In the unsupervised approach, we run the HITS algorithm on document graphs under the assumption that the top-ranked nodes should represent the document keywords. Our experiments on a collection of benchmark summaries show that given a set of summarized training documents, the supervised classification provides the highest keyword identification accuracy, while the highest F-measure is reached with a simple degree-based ranking. In addition, it is sufficient to perform only the first iteration of HITS rather than running it to its convergence. <s> BIB004
A coherent based approach basically deals with the cohesion relations among the words. Cohesion relations among elements in a text: reference, ellipsis, substitution, conjunction, and lexical cohesion BIB001 . Lexical chain (LC) BIB003 , WordNet (WN)  BIB002 , lexical chain score of a word (LCS) BIB003 , direct lexical chain score of a word (DLCS) BIB003 , lexical chain span score of a word (LCSS) BIB003 , direct lexical chain span score of a word (DLCSS) BIB003 , Rhetorical Structure Theory (RST)  [116] BIB004 .