Automatic Keyword Extraction for Text Summarization: A Survey <s> Machine Learning Approach <s> A sentence extract summary of a document is a subset of the document's sentences that contains the main ideas in the document. We present an approach to generating such summaries, a hidden Markov model that judges the likelihood that each sentence should be contained in the summary. We compare the results of this method with summaries generated by humans, showing that we obtain significantly higher agreement than do earlier methods. <s> BIB001 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Machine Learning Approach <s> In this paper, experiments on automatic extraction of keywords from abstracts using a supervised machine learning algorithm are discussed. The main point of this paper is that by adding linguistic knowledge to the representation (such as syntactic features), rather than relying only on statistics (such as term frequency and n-grams), a better result is obtained as measured by keywords previously assigned by professional indexers. In more detail, extracting NP-chunks gives a better precision than n-grams, and by adding the PoS tag(s) assigned to the term as a feature, a dramatic improvement of the results is obtained, independent of the term selection approach applied. <s> BIB002 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Machine Learning Approach <s> This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method. <s> BIB003
Keyword extraction can also be seen as a learning problem. This approach requires manually annotated training data and training models. Hidden Markov model BIB001 , support vector machine (SVM) BIB003 , naive Bayes (NB)  , bagging BIB002 , etc. are commonly used training models in these approaches. In the second phase, the document whose keywords are to be extracted is given as inputs to the model, which then extracts the keywords that best fit the model's training. One of the most famous algorithms in this approach is the keyword extraction algorithm (KEA)  . In this approach, the article is first converted into a graph where each word is treated as a node, and whenever two words appear in the same sentence, the nodes are connected with an edge for each time they appear together. Then the number of edges connecting the vertices are converted into scores and are clustered accordingly. The cluster heads are treated as keywords. Bayesian algorithms use the Bayes classifier to classify the word into two categories: keyword or not a keyword depending on how it is trained. GenEx  is another tool in this approach.