A Survey of Text Summarization Extractive Techniques <s> I. 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> BIB001 </s> A Survey of Text Summarization Extractive Techniques <s> I. INTRODUCTION <s> As part of evaluating a summary automatically, it is usual to determine how much of the contents of one or more human-produced ‘ideal’ summaries it contains. Past automated methods such as ROUGE compare using fixed word ngrams, which are not ideal for a variety of reasons. In this paper we describe a framework in which summary evaluation measures can be instantiated and compared, and we implement a specific evaluation method using very small units of content, called Basic Elements, that address some of the shortcomings of ngrams. This method is tested on DUC 2003, 2004, and 2005 systems and produces very good correlations with human judgments. <s> BIB002 </s> A Survey of Text Summarization Extractive Techniques <s> I. INTRODUCTION <s> In this paper we first analyze some state of the art methods for text summarization. We discuss what the main disadvantages of these methods are and then propose a new method using fuzzy logic. Comparisons of results show that our method beats most methods which use machine learning as their core. <s> BIB003
Text summarization  has become an important and timely tool for assisting and interpreting text information in today's fast-growing information age. It is very difficult for human beings to manually summarize large documents of text. There is an abundance of text material available on the internet. However, usually the Internet provides more information than is needed. Therefore, a twofold problem is encountered: searching for relevant documents through an overwhelming number of documents available, and absorbing a large quantity of relevant information. The goal of automatic text summarization is condensing the source text into a shorter version preserving its information content and overall meaning. A summary  can be employed in an indicative way as a pointer to some parts of the original document, or in an informative way to cover all relevant information of the text. In both cases the most important advantage of using a summary is its reduced reading time. A good summary system should reflect the diverse topics of the document while keeping redundancy to a minimum. Summarization tools may also search for headings and other markers of subtopics in order to identify the key points of a document. Microsoft Word's AutoSummarize function is a simple example of text summarization. Text Summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An Abstractive summarization BIB001 [33] attempts to develop an understanding of the main concepts in a document and then express those concepts in clear natural language. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. This paper focuses on extractive text summarization methods. Extractive summaries BIB003 are formulated by extracting key text segments (sentences or passages) from the text, based on statistical analysis of individual or mixed surface level features such as word/phrase frequency, location or cue words to locate the sentences to be extracted. The "most important" content is treated as the "most frequent" or the "most favorably positioned" content. Such an approach thus avoids any efforts on deep text understanding. They are conceptually simple, easy to implement. Extractive text summarization process  can be divided into two steps: 1) Pre Processing step and 2) Processing step. Pre Processing is structured representation of the original text. It usually includes: a) Sentences boundary identification. In English, sentence boundary is identified with presence of dot at the end of sentence. b) Stop-Word Elimination-Common words with no semantics and which do not aggregate relevant information to the task are eliminated. c) Stemming-The purpose of stemming is to obtain the stem or radix of each word, which emphasize its semantics. In Processing step, features influencing the relevance of sentences are decided and calculated and then weights are assigned to these features using weight learning method. Final score of each sentence is determined using Feature-weight equation. Top ranked sentences are selected for final summary. Problems with the extractive summary  [47] are: 1. Extracted sentences usually tend to be longer than average. Due to this, parts of the segments that are not essential for summary also get included, consuming space. 2. Important or relevant information is usually spread across sentences, and extractive summaries cannot capture this (unless the summary is long enough to hold all those sentences). 3. Conflicting information may not be presented accurately.  . Pure extraction often leads to problems in overall coherence of the summary-a frequent issue concerns "dangling" anaphora. Sentences often contain pronouns, which lose their referents when extracted out of context. Worse yet, stitching together decontextualized extracts may lead to a misleading interpretation of anaphors (resulting in an inaccurate representation of source information, i.e., low fidelity). Similar issues exist with temporal expressions. These problems become more severe in the multi-document case, since extracts are drawn from different sources. A general approach to addressing these issues involves post-processing extracts, for example, replacing pronouns with their antecedents, replacing relative temporal expression with actual dates, etc. Problems with the abstractive summary  are: The biggest challenge for abstractive summary is the representation problem. Systems' capabilities are constrained by the richness of their representations and their ability to generate such structures-systems cannot summarize what their representations cannot capture. In limited domains, it may be feasible to devise appropriate structures, but a general-purpose solution depends on open-domain semantic analysis. Systems that can truly "understand" natural language are beyond the capabilities of today's technology. Summary evaluation  [36] BIB002 is a very important aspect for text summarization. Generally, summaries can be evaluated using intrinsic or extrinsic measures. While intrinsic methods attempt to measure summary quality using human evaluation and extrinsic methods measure the same through a task-based  performance measure such the information retrieval-oriented task. Newsblaster is a good example of a text summarizer, that helps users find the news that is of the most interest to them. The system automatically collects, clusters, categorizes, and summarizes news from several sites on the web (CNN, Reuters, Fox News, etc.) on a daily basis, and it provides users a user-friendly interface to browse the results.