Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> Excerpts of technical papers and magazine articles that serve the purposes of conventional abstracts have been created entirely by automatic means. In the exploratory research described, the complete text of an article in machine-readable form is scanned by an IBM 704 data-processing machine and analyzed in accordance with a standard program. Statistical information derived from word frequency and distribution is used by the machine to compute a relative measure of significance, first for individual words and then for sentences. Sentences scoring highest in significance are extracted and printed out to become the "auto-abstract." <s> BIB001 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> When humans produce summaries of documents, they do not simply extract sentences and concatenate them. Rather, they create new sentences that are grammatical, that cohere with one another, and that capture the most salient pieces of information in the original document. Given that large collections of text/abstract pairs are available online, it is now possible to envision algorithms that are trained to mimic this process. In this paper, we focus on sentence compression, a simpler version of this larger challenge. We aim to achieve two goals simultaneously:our compressions should be grammatical, and they should retain the most important pieces of information. These two goals can conflict. We devise both noisy-channel and decision-tree approaches to the problem, and we evaluate results against manual compressions and a simple baseline. <s> BIB002 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> The presentation of query biased document snippets as part of results pages presented by search engines has become an expectation of search engine users. In this paper we explore the algorithms and data structures required as part of a search engine to allow efficient generation of query biased snippets. We begin by proposing and analysing a document compression method that reduces snippet generation time by 58% over a baseline using the zlib compression library. These experiments reveal that finding documents on secondary storage dominates the total cost of generating snippets, and so caching documents in RAM is essential for a fast snippet generation process. Using simulation, we examine snippet generation performance for different size RAM caches. Finally we propose and analyse document reordering and compaction, revealing a scheme that increases the number of document cache hits with only a marginal affect on snippet quality. This scheme effectively doubles the number of documents that can fit in a fixed size cache. <s> BIB003 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at . The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text. <s> BIB004 </s> Text Summarization Techniques: A Brief 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> BIB005 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> We learn a joint model of sentence extraction and compression for multi-document summarization. Our model scores candidate summaries according to a combined linear model whose features factor over (1) the n-gram types in the summary and (2) the compressions used. We train the model using a margin-based objective whose loss captures end summary quality. Because of the exponentially large set of candidate summaries, we use a cutting-plane algorithm to incrementally detect and add active constraints efficiently. Inference in our model can be cast as an ILP and thereby solved in reasonable time; we also present a fast approximation scheme which achieves similar performance. Our jointly extracted and compressed summaries outperform both unlearned baselines and our learned extraction-only system on both ROUGE and Pyramid, without a drop in judged linguistic quality. We achieve the highest published ROUGE results to date on the TAC 2008 data set. <s> BIB006 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> This paper contains a large literature review in the research field of Text Summarisation (TS) based on Human Language Technologies (HLT). TS helps users manage the vast amount of information available, by condensing documents' content and extracting the most relevant facts or topics included in them. The rapid development of emerging technologies poses new challenges to this research field, which still need to be solved. Therefore, it is essential to analyse its progress over the years, and provide an overview of the past, present and future directions, highlighting the main advances achieved and outlining remaining limitations. With this purpose, several important aspects are addressed within the scope of this survey. On the one hand, the paper aims at giving a general perspective on the state-of-the-art, describing the main concepts, as well as different summarisation approaches, and relevant international forums. Furthermore, it is important to stress upon the fact that the birth of new requirements and scenarios has led to new types of summaries with specific purposes (e.g. sentiment-based summaries), and novel domains within which TS has proven to be also suitable for (e.g. blogs). In addition, TS is successfully combined with a number of intelligent systems based on HLT (e.g. information retrieval, question answering, and text classification). On the other hand, a deep study of the evaluation of summaries is also conducted in this paper, where the existing methodologies and systems are explained, as well as new research that has emerged concerning the automatic evaluation of summaries' quality. Finally, some thoughts about TS in general and its future will encourage the reader to think of novel approaches, applications and lines to conduct research in the next years. The analysis of these issues allows the reader to have a wide and useful background on the main important aspects of this research field. <s> BIB007 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> Numerous approaches for identifying important content for automatic text summarization have been developed to date. Topic representation approaches first derive an intermediate representation of the text that captures the topics discussed in the input. Based on these representations of topics, sentences in the input document are scored for importance. In contrast, in indicator representation approaches, the text is represented by a diverse set of possible indicators of importance which do not aim at discovering topicality. These indicators are combined, very often using machine learning techniques, to score the importance of each sentence. Finally, a summary is produced by selecting sentences in a greedy approach, choosing the sentences that will go in the summary one by one, or globally optimizing the selection, choosing the best set of sentences to form a summary. In this chapter we give a broad overview of existing approaches based on these distinctions, with particular attention on how representation, sentence scoring or summary selection strategies alter the overall performance of the summarizer. We also point out some of the peculiarities of the task of summarization which have posed challenges to machine learning approaches for the problem, and some of the suggested solutions. <s> BIB008 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains. <s> BIB009 </s> Text Summarization Techniques: A Brief Survey <s> INTRODUCTION <s> The goals of the Triple Aim of health care and the goals of P4 medicine outline objectives that require a significant health informatics component. However, the goals do not provide specifications about how all of the new individual patient data will be combined in meaningful ways and with data from other sources, like epidemiological data, to promote the health of individuals and society. We seem to have more data than ever before but few resources and means to use it efficiently. We need a general, extensible solution that integrates and homogenizes data of disparate origin, incompatible formats, and multiple spatial and temporal scales. To address this problem, we introduce the Scientific Knowledge Extraction from Data (SKED) architecture, as a technology-agnostic framework to minimize the overhead of data integration, permit reuse of analytical pipelines, and guarantee reproducible quantitative results. The SKED architecture consists of a Resource Allocation Service to locate resources, and the definition of data primitives to simplify and harmonize data. SKED allows automated knowledge discovery and provides a platform for the realization of the major goals of modern health care. <s> BIB010
With the dramatic growth of the Internet, people are overwhelmed by the tremendous amount of online information and documents. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). arXiv, July 2017, USA © 2017 Copyright held by the owner/author(s). ACM ISBN 978-x-xxxx-xxxx-x/YY/MM. . . $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn This expanding availability of documents has demanded exhaustive research in the area of automatic text summarization. According to Radef et al.  a summary is defined as "a text that is produced from one or more texts, that conveys important information in the original text(s), and that is no longer than half of the original text(s) and usually, significantly less than that". Automatic text summarization is the task of producing a concise and fluent summary while preserving key information content and overall meaning. In recent years, numerous approaches have been developed for automatic text summarization and applied widely in various domains. For example, search engines generate snippets as the previews of the documents BIB003 . Other examples include news websites which produce condensed descriptions of news topics usually as headlines to facilitate browsing or knowledge extractive approaches BIB009 BIB004 BIB010 . Automatic text summarization is very challenging, because when we as humans summarize a piece of text, we usually read it entirely to develop our understanding, and then write a summary highlighting its main points. Since computers lack human knowledge and language capability, it makes automatic text summarization a very difficult and non-trivial task. Automatic text summarization gained attraction as early as the 1950s. An important research of these days was BIB001 for summarizing scientific documents. Luhn et al. BIB001 introduced a method to extract salient sentences from the text using features such as word and phrase frequency. They proposed to weight the sentences of a document as a function of high frequency words, ignoring very high frequency common words. Edmundson et al.  described a paradigm based on key phrases which in addition to standard frequency depending weights, used the following three methods to determine the sentence weight: (1) Cue Method: The relevance of a sentence is calculated based on the presence or absence of certain cue words in the cue dictionary. (2) Title Method: The weight of a sentence is computed as the sum of all the content words appearing in the title and headings of a text. (3) Location Method: This method assumes that sentences appearing in the beginning of document as well as the beginning of individual paragraphs have a higher probability of being relevant. Since then, many works have been published to address the problem of automatic text summarization (see BIB005  for more information about more advanced techniques until 2000s). In general, there are two different approaches for automatic summarization: extraction and abstraction. Extractive summarization methods work by identifying important sections of the text and generating them verbatim; thus, they depend only on extraction of sentences from the original text. In contrast, abstractive summarization methods aim at producing important material in a new way. In other words, they interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. Even though summaries created by humans are usually not extractive, most of the summarization research today has focused on extractive summarization. Purely extractive summaries often times give better results compared to automatic abstractive summaries BIB005 . This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which are relatively harder than data-driven approaches such as sentence extraction. As a matter of fact, there is no completely abstractive summarization system today. Existing abstractive summarizers often rely on an extractive preprocessing component to produce the abstract of the text BIB006 BIB002 . Consequently, in this paper we focus on extractive summarization methods and provide an overview of some of the most dominant approaches in this category. There are a number of papers that provide extensive overviews of text summarization techniques and systems BIB007 BIB008   .