Automatic Keyword Extraction for Text Summarization: A Survey <s> Abstractive Text Summarization <s> Abstract As electronic information access becomes the norm, and the variety of retrievable material increases, automatic methods of summarizing or condensing text will become critical. This paper describes a system that performs domain-independent automatic condensation of news from a large commercial news service encompassing 41 different publications. This system was evaluated against a system that condensed the same articles using only the first portion of the texts (the lead), up to the target length of the summaries. Three lengths of articles were evaluated for 250 documents by both systems, totalling 1500 suitability judgements in all. The outcome of perhaps the largest evaluation of human vs machine summarization performed to date was unexpected. The lead-based summaries outperformed the “intelligent” summaries significantly, achieving acceptability ratings of over 90%, compared to 74.4%. This paper briefly reviews the literature, details the implications of these results, and addresses the remaining hopes for content-based summarization. We expect the results presented here to be useful to other researchers currently investigating the viability of summarization through sentence selection heuristics. <s> BIB001 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Abstractive 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> BIB002 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Abstractive 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> BIB003 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Abstractive Text Summarization <s> Automatic extraction of temporal relations between event pairs is an important task for several natural language processing applications such as Question Answering, Information Extraction, and Summarization. Since most existing methods are supervised and require large corpora, which for many languages do not exist, we have concentrated our efforts to reduce the need for annotated data as much as possible. This paper presents two different algorithms towards this goal. The first algorithm is a weakly supervised machine learning approach for classification of temporal relations between events. In the first stage, the algorithm learns a general classifier from an annotated corpus. Then, inspired by the hypothesis of "one type of temporal relation per discourse", it extracts useful information from a cluster of topically related documents. We show that by combining the global information of such a cluster with local decisions of a general classifier, a bootstrapping cross-document classifier can be built to extract temporal relations between events. Our experiments show that without any additional annotated data, the accuracy of the proposed algorithm is higher than that of several previous successful systems. The second proposed method for temporal relation extraction is based on the expectation maximization (EM) algorithm. Within EM, we used different techniques such as a greedy best-first search and integer linear programming for temporal inconsistency removal. We think that the experimental results of our EM based algorithm, as a first step toward a fully unsupervised temporal relation extraction method, is encouraging. <s> BIB004 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> Abstractive 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> BIB005
In this procedure, a machine needs to comprehend the idea of all the input documents and then deliver summary with its particular sentences  BIB001 developed an abstractive summarization system that analyses the statistical corpus and extracts the signature words from the corpus. Then it assigns the weight for all the signature words. Based on the extracted signature words, they assign the weight to the sentences and select few top weighted sentences as the summary. Daume et al.  developed an abstractive summarization system that maps all the documents into database-like representation. Further, it classifies into four categories: a single person, single event, multiple event, and natural disaster. It generates a short headline using a set of predefined templates. It generates summaries by extracting sentences from the database. BIB005 designed a system for automatic keyword extraction for text summarization using hidden Markov model. The learning process was supervised, it used human annotated keyword set to train the model. Mirroshandel et al. BIB004 used a set of labeled dataset to train the system for the classification of temporal relations between events. Aramaki et al. BIB002 destined a supervised learning based extractive text summarizer that identifies the negative event and also investigates what kind of information is helpful for negative event identification. An SVM classifier is used to distinguish negative events from other events. BIB004 proposed a method for temporal relation extraction based on the Expectation-Maximization (EM) algorithm. Within EM, they used different techniques such as a greedy best-first search and integer linear programming for temporal inconsistency removal. The EM-based approach was a fully unsupervised temporal relation based extraction for text summarization. Alguliev et al. BIB003 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.