Automatic Keyword Extraction for Text Summarization: A Survey <s> Extractive Text Summarization <s> With the rapidly growing use of electronic health records, the possibility of large-scale clinical information extraction has drawn much attention. It is not, however, easy to extract information because these reports are written in natural language. To address this problem, this paper presents a system that converts a medical text into a table structure. This system’s core technologies are (1) medical event recognition modules and (2) a negative event identification module that judges whether an event actually occurred or not. Regarding the latter module, this paper also proposes an SVM-based classifier using syntactic information. Experimental results demonstrate empirically that syntactic information can contribute to the method’s accuracy. <s> BIB001 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Extractive Text Summarization <s> In paper, we propose an unsupervised text summarization model which generates a summary by extracting salient sentences in given document(s). In particular, we model text summarization as an integer linear programming problem. One of the advantages of this model is that it can directly discover key sentences in the given document(s) and cover the main content of the original document(s). This model also guarantees that in the summary can not be multiple sentences that convey the same information. The proposed model is quite general and can also be used for single- and multi-document summarization. We implemented our model on multi-document summarization task. Experimental results on DUC2005 and DUC2007 datasets showed that our proposed approach outperforms the baseline systems. <s> BIB002 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Extractive Text Summarization <s> We show that by making use of information common to document sets belonging to a common category, we can improve the quality of automatically extracted content in multi-document summaries. This simple property is widely applicable in multi-document summarization tasks, and can be encapsulated by the concept of category-specific importance (CSI). Our experiments show that CSI is a valuable metric to aid sentence selection in extractive summarization tasks. We operationalize the computation CSI of sentences through the introduction of two new features that can be computed without needing any external knowledge. We also generalize this approach, showing that when manually-curated document-to-category mappings are unavailable, performing automatic categorization of document sets also improves summarization performance. We have incorporated these features into a simple, freely available, open-source extractive summarization system, called SWING. In the recent TAC-2011 guided summarization task, SWING outperformed all other participant summarization systems as measured by automated ROUGE measures. <s> BIB003 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Extractive Text Summarization <s> Summarization is the process of reducing a text document to create a summary that retains the most important points of the original document. Extractive summarizers work on the given text to extract sentences that best convey the message hidden in the text. Most extractive summarization techniques revolve around the concept of finding keywords and extracting sentences that have more keywords than the rest. Keyword extraction usually is done by extracting relevant words having a higher frequency than others, with stress on important ones'. Manual extraction or annotation of keywords is a tedious process brimming with errors involving lots of manual effort and time. In this paper, we proposed an algorithm to extract keyword automatically for text summarization in e-newspaper datasets. The proposed algorithm is compared with the experimental result of articles having the similar title in four different e-Newspapers to check the similarity and consistency in summarized results. <s> BIB004
In this procedure, summarizer discovers more critical information (either words or sentences) from input document to make the summary of the corresponding document [ BIB003 . In this process, it uses statistical and linguistic features of the sentences to decide the most relevant sentences in the given input document. Thomas et al. BIB004 designed a hybrid model based extractive summarizer using machine learning and simple statistical method for keyword extraction from e-Newspaper article. Min et al. BIB003 used freely available, open-source extractive summarization system, called SWING to summarize the text in multi-document. They used information which is common to document sets belonging to a common category as a feature and encapsulated the concept of category-specific importance (CSI). They showed that CSI is a valuable metric to aid sentence selection in extractive  parsing algorithm to determine discourse structure of the text of given input, determine partial ordering on the elementary and parenthetical units of the text. Erkan et al.  developed an extractive summarization environment. It consists of three steps: feature extractor, the feature vector, and reranker. Features are Centroid, Position, Length Cutoff, SimWithFirst, LexPageRank, and QueryPhraseMatch. Alguliev et al. BIB002 developed an unsupervised learning based extractive summarizer that optimizes three properties: relevance, redundancy, and length. It split documents into sentences and select salient sentences from the document. Aramaki et al. BIB001 