Automatic Keyword Extraction for Text Summarization: A Survey <s> INTRODUCTION <s> This paper consists of three parts: a preliminary typology of summaries in general; a description of the current and planned modules and performance of the SUMMARIST automated multilingual text summarization system being built sat ISI, and a discussion of three methods to evaluate summaries. <s> BIB001 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> INTRODUCTION <s> From the Publisher: ::: Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task.. "Until now there has been no state-of-the-art collection of the most important writings in automatic text summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections. Classical Approaches, Corpus-Based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas. <s> BIB002 </s> Automatic Keyword Extraction for Text Summarization: A Survey <s> INTRODUCTION <s> We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents. <s> BIB003
In the era of internet, plethora of online information are freely available for readers in the form of e-Newspapers, journal articles, technical reports, transcription dialogues etc. There are huge number of documents available in above digital media and extracting only relevant information from all these media is a tedious job for the individuals in stipulated time. There is a need for an automated system that can extract only relevant information from these data sources. To achieve this, one need to mine the text from the documents. Text mining is the process of extracting large quantities of text to derive high-quality information. Text mining deploys some of the techniques of natural language processing (NLP) such as parts-of-speech (POS) tagging, parsing, N-grams, tokenization, etc., to perform the text analysis. It includes tasks like automatic keyword extraction and text summarization. Automatic keyword extraction is the process of selecting words and phrases from the text document that can at best project the core sentiment of the document without any human intervention depending on the model [1] . The target of automatic keyword extraction is the application of the power and speed of current computation abilities to the problem of access and recovery, stressing upon information organization without the added costs of human annotators. Summarization is a process where the most salient features of a text are extracted and compiled into a short abstract of the original document BIB001 . According to Mani and Maybury BIB002 , text summarization is the process of distilling the most important information from a text to produce an abridged version for a particular task and user. Summaries are usually around 17\% of the original text and yet contain everything that could have been learned from reading the original article BIB003 . In the wake of big data analysis, summarization is an efficient and powerful technique to give a glimpse of the whole data. The text summarization can be achieved in two ways namely, abstractive summary and extractive summary. The abstractive summary is a topic under tremendous research; however, no standard algorithm has been achieved yet. These summaries are derived from learning what was expressed in the article and then converting it into a form expressed by the computer. It resembles how a human would summarize an article after reading it. Whereas, extractive summary extract details from the original article itself and present it to the reader. Figure 3 . This literature also discusses about different databases used for text summarization such as DUC, TAC, MEDLINE, etc. along with differnt evaluation matrices such as precision, recall, and ROUGE series. Finally, it discusses briefly about issues and research challenges faced by researchers followed by future direction of text summarization. The rest of this paper is organized as follows. Section 2 presents a review on automatic keyword extraction Section 3 presents a review on text summarization process. A review on automatic text summarization methologies are given in Section 4. The details about different databases are described in Section 5. Section 6 explains about evaluation matrices for text summarization. Finally, the conclusion with future direction is drawn in Section 7.