We are building an interactive visual text analysis tool that aids users in analyzing large collections of text. Unlike existing work in visual text analytics, which focuses either on developing sophisticated text analytic techniques or inventing novel text visualization metaphors, ours tightly integrates state-of-the-art text analytics with interactive visualization to maximize the value of both. In this ...

This demonstration illustrates an information aggregation and summarization service for social sensing applications. Social sensing, using mobile phones and other networked devices in the possession of individuals, has gained significant popularity in recent years. In some cases, the information collected is structured, such as numeric data from temperature sensors, accelerometers, or GPS devices. Aggregate statistical properties, such as expected values, ...

A discourse parser is a natural language processing system which can represent the organization of a document based on a rhetorical structure tree-one of the key data structures enabling applications such as text summarization, question answering and dialogue generation. Computational linguistics researchers currently rely on manually exploring and comparing the discourse structures to get intuitions for improving parsing algorithms. ...

The features are considered the cornerstone of text summarization. The most important issue is what feature to be considered in a text summarization process. Including all the features in the summarization process may not be considered as an optimal solution. Therefore, other methods need to be deployed. In this paper, random five features used and investigated using a (pseudo) Genetic ...

Natural Language Generation (NLG) focuses on the generation of written texts in natural language from some underlying semantic representation of information. A new semantic representation called Rich Semantic Graph (RSG) has been proposed to be used as an intermediate representation during recent research for Natural Language processing applications. In this paper, a new model to generate an English text from ...

Novelty detection is already used in many Natural Processing Language (NLP) applications, such as information retrieval systems, Web search engines, text summarization, question answering systems …etc. This study aims to detect novel Arabic sentence level information patterns. The Length Adjusted (LA) model is based on sentence level information patterns is used, which depends on the sentence length. Test results show ...

More and more today, people are engaging in conversations via email, blogs, discussion forums, text messaging and other social media. A person may want to archive these conversations and later retrieve information about what was discussed, or analyze a conversation in real-time. What topics are covered in these conversations? What opinions are people expressing? Have any decisions been made? ...

The vast amount of user-generated content on the Web has increased the need for handling the problem of automatically processing content in web pages. The segmentation of web pages and noise (non-informative segment) removal are important pre-processing steps in a variety of applications such as sentiment analysis, text summarization and information retrieval. Currently, these two tasks tend ...

This paper describes a system that produces extractive summaries of Bengali news documents. The ultimate objective of produced summaries is defined as helping readers to determine whether they would be interested in reading a particular document. To this end, the summary aims to provide a reader with an idea about the theme of a document without revealing the in-depth ...

In this paper, we consider the problem of diversity in ranking of the nodes in a graph. The task is to pick the top-k nodes in the graph which are both 'central' and 'diverse'. Many graph-based models of NLP like text summarization, opinion summarization involve the concept of diversity in generating the summaries. We develop a novel method ...

The continuous information explosion through the Internet and all information sources makes it necessary to perform all information processing activities automatically in quick and reliable manners. In this paper, we proposed and implemented a method to automatically create and Index for books written in Arabic language. The process depends largely on text summarization and abstraction processes to collect main topics ...

We consider the problem of learning to summarize images by text and visualize text utilizing images, which we call Mutual-Summarization. We divide the web image-text data space into three subspaces, namely pure image space (PIS), pure text space (PTS) and image-text joint space (ITJS). Naturally, we treat the ITJS as a knowledge base. For summarizing images by ...

The Rhetorical Structure Theory (RST) is a descriptive theory of a major aspect of the structure of natural text. It is applied in English as well as other languages such as, French and Japanese but there are still no clear efforts to apply RST in Arabic. This paper provides a framework to apply RST in Arabic, in order to improve ...

Published in 2012]]>http://journalogy.net/Publication/59062685Mon, 20 May 2013 02:36:51 GMT59062685Youn S. Kimview publication

With the overwhelming amount of textual information available in electronic formats on the web, there is a need for an efficient text summarizer capable of condensing large bodies of text into shorter versions while keeping the relevant information intact. Such a technology would allow users to get their information in a shortened form, saving valuable time. Since 1997, Microsoft Word ...

Contextual advertising is a type of Web advertising, which, given the URL of a Web page, aims to embed into the page the most relevant textual ads available. For static pages that are displayed repeatedly, the matching of ads can be based on prior analysis of their entire content; however, often ads need to be matched to new or dynamically ...

This research is conducted to accommodate the needs of visually impaired people through an intelligent system, which reads textual information on papers and produces corresponding voice. Indonesian Automated Document Reader (I-ADR) is operated via a voice-based user interface to scan a document page. Textual information from the scanned page is then extracted using Optical Character Recognition (OCR) techniques. ...

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 ...

We present an approach of identifying the most prominent text/sentences using various shallow linguistic features, taking degree of connectiveness among the text units into consideration so as to minimize the poorly linked sentences in the resulting summary. As per the limitations of the current summarizing systems, the summary generated by those systems contains poorly linked sentences and are not ...

Automatic Document Summarization is a highly interdisciplinary research area related with computer science as well as cognitive psychology. This Summarization is to compress an original document into a summarized version by extracting almost all of the essential concepts with text mining techniques. This research focuses on developing a statistical automatic text summarization approach, K- mixture probabilistic model, to enhancing the ...

With ever growing content on World Wide Web, it has been increasingly difficult for users to search for relevant information. A rough estimation of world's famous search engine Google in year 2010 revealed that the total size of internet has now turned to 2 petabytes. Search engines that are supposed to satisfy user's information need, has too much ...

This paper presents a new generic text summarization method using Non-negative Matrix Factorization (NMF) to estimate sentence relevance. Proposed sentence relevance estimation is based on normalization of NMF topic space and further weighting of each topic using sentences representation in topic space. The proposed method shows better summarization quality and performance than state of the art methods on DUC ...

BackgroundTo provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms.

The Internet provides many sources of different opinions, expressed through user reviews of products, blogs, and forum discussions. Systems which could automatically summarize these opinions would be immensely useful for those who wish to use this information to make decisions. The previous work in automatic summarization has completely focused on extractive summarization, in which key sentences are identified from the ...

Text summarization techniques have been found to be effective with regard to helping users find relevant information faster. The effectiveness and efficiency of a user's performance in an information-seeking task can greatly be improved if he/she needs to only look at a summary that includes the relevant infor- mation presented in his/her preferred manner. On the ...

With increasing of the availability information on the Web, document summarization technologies become very important research. There are many techniques using latent semantic analysis (LSA) and non-negative matrix factorization ($MF), but there are no researches on matrix reduction and computation speeding for $MF method. Therefore, this paper utilizes the generic knowledge of expressions from newspapers to extract important sentences ...

In this study parsimonious language models were used to construct word clouds of the proceedings of the European Parliament. Multiple design choices had to be made and are discussed. Important features are stemming during tokenization, including bigrams into the word cloud and multilingualism. Also, the original parsimonious language models were extended with an additional term dampening unigrams that already occurred ...

Text summarization solves the problem of presenting the information needed by a user in a compact form. There are different approaches to creating well-formed summaries. One of the newest methods is the Latent Semantic Analysis (LSA). In this paper, different LSA-based summarization algorithms are explained, two of which are proposed by the authors of this paper. The algorithms ...

With the rapid growth of internet, huge amount of data is available online. The ability to draw useful information from this digital data is quite challenging. The task of exploring and extracting information from native languages available on line is very much a useful task. The work presented here focuses on sentence level classification in the Kannada language. The most ...

Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) ...

Journal: International Journal of Computer Processing of Oriental Languages - IJCPOL, vol. 23, no. 1, pp. 39-65, 2011]]>http://journalogy.net/Publication/56904570Mon, 20 May 2013 02:36:31 GMT56904570Jairo Aponte, Andrian Marcusview publication

Analyzing candidate traceability links is a difficult, time consuming and error prone task, as it usually requires a detailed study of a long list of software artifacts of various kinds. One option to alleviate this problem is to select the most important features of the software artifacts that the developers would investigate. We discuss in this position paper how text ...

The World Wide Web has caused an information explosion. Readers are often drowned in information while starved of knowledge. Readers are bombarded with too many lengthy documents where shorter summarized texts would be preferable. This paper presents a hybrid Harmony Particle Swarm Optimization (PSO) framework in an Extractive Text Summarizer to tackle the information overload problem. Particle Swarm Optimization is ...

As the representative of new media, microblog boosts for its real time characteristic and rich content. Using the summaries of a hot topic to display the topic can provide many important messages and background knowledge to the users. Since the short text feature and low costs of transmission, the messages of microblogs transmit very fast and tend to produce explosive ...

This paper describes the summarization of textual material about locations in the context of a geo-spatial information display system. Both structured data and unstructured web pages are linked to maps. When the amount of associated textual data is large, it is organized and summarized before display. A hierarchical summarization framework, conditioned on the small space available for display, has ...

These days a number of meeting minutes of various organizations are publicly available and the interest in these documents by people is increasing. However it is time-consuming and tedious to read and understand whole documents even if the documents can be accessed easily. In addition, what most people want from meeting minutes is to catch the main issues of ...

Learning to Generate a Table-of-Contents with Supportive Knowledge

Text segmentation is a fundamental problem in natural language processing, which has application in information retrieval, question answering, and text summarization. Almost previous works on unsupervised text segmentation are based on the assumption of lexical cohesion, which is indicated by relations between words in the two units of text. However, they only take into account the reiteration, which is a ...

Published in 2011]]>http://journalogy.net/Publication/39259911Mon, 20 May 2013 02:36:23 GMT39259911Shamima Mithun, Leila Kosseimview publication

It is widely accepted that in a text, sentences and clauses cannot be understood in isolation but in relation with each other through discourse relations that may or may not be explicitly marked. Discourse relations have been found useful in many applications such as machine translation, text summarization, and question answering; however, they are often not considered in computational language ...

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 ...

Published in 2011]]>http://journalogy.net/Publication/56926822Mon, 20 May 2013 02:36:20 GMT56926822Dexi Liu, Shihan Wuview publication

In document-center XML dataset, an element may contain so many text that users have to spend enough time to judge the elements returned by XML search engine are valuable or not. Query-orient XML summarization system aim to provide users a brief and readable substitution of the original retrieved elements according to the user's query, which can relieve ...

Automatic text summarization is a data reduction process to exclude unnecessary details and present important information in a shorter version. One way to summarize document is by extracting important sentences in the document. To select suitable sentences, a numerical rank is assigned to each sentence based on a sentence scoring approach. Highly ranked sentences are used for the summary. This ...

Conference: Database and Expert Systems Applications - DEXA, pp. 172-176, 2011]]>http://journalogy.net/Publication/56902274Mon, 20 May 2013 02:36:17 GMT56902274M. K. Dalal, M. A. Zaveriview publication

Automatic Text Summarization is a specialized text mining task of generating a summary or abstract from single or multiple input text documents. Various heuristic and semi-supervised learning methods have been explored by researchers in this field to generate generic as well as user-oriented summaries. This paper examines the effectiveness of well-known summarization heuristics when applied to the ...

Cross-language information retrieval (CLIR) is a subfield of information retrieval dealing with retrieving information written in a language different from the language of the user's query. The domain of CLIR is crucial in the future as vast amount of content in the web is in English. There is a need for mechanisms that can retrieve the content from ...

Published in 2011]]>http://journalogy.net/Publication/48861150Mon, 20 May 2013 02:36:14 GMT48861150Feng Jin, Min-Lie Huang, Xiao-Yan Zhuview publication

Although the goal of traditional text summarization is to generate summaries with diverse information, most of those applications have no explicit definition of the information structure. Thus, it is difficult to generate truly structure-aware summaries because the information structure to guide summarization is unclear. In this paper, we present a novel framework to generate guided summaries for product reviews. ...

Automatic text summarization is an important and challenging problem. Over the years, the amount of text available electronically has grown exponentially. This growth has created a huge demand for automatic methods and tools for text summarization. We can think of automatic summarization as a type of information compression. To achieve such compression, better modelling and understanding of document structures and ...

Over the past decade, online advertising became the principal economic force behind many an Internet service, from major search engines to globe-spanning social networks to blogs. There is often a tension between online advertising and user experience, but on the other hand, advertising revenue enables a myriad of free Web services to the public and fosters a great deal ...

Multimedia documents, especially the text documents are occupying an important time in our daily life. Reading the summery of documents will allow us to spend less time and understand the document faster. A sumary consist of the subset sentences of documents and theyneeds to cover the main content of document as a brief. In this paper, we have proposed an ...

The insertion of linguistic material into document sentences to create new sentences is a common activity in document abstracting. We investigate a transformation-based learning method to simulate this type of operation relevant for text summarization. Our work is framed on a theory of transformation-based abstracting where an initial text summary is transformed into an abstract by the application ...

Published in 2011]]>http://journalogy.net/Publication/60094409Mon, 20 May 2013 02:36:08 GMT60094409Patrick Matthewsview publication

This Streamlined Approach for Environmental Restoration (SAFER) Plan addresses the actions needed to achieve closure for Corrective Action Unit (CAU) 465, Hydronuclear, identified in the Federal Facility Agreement and Consent Order (FFACO). Corrective Action Unit 465 comprises the following four corrective action sites (CASs) located in Areas 6 and 27 of the Nevada National Security Site: (1) 00-23-01, ...

This text summarizes the keynote presentation that is based on the full-length paper of the same title. Dr. Fréchette's oral presentation should not be seen as a summary of the “Brainstorm paper” but a glance at some major accomplishments, hinrances and still remaining questions relative to nanodielectrics. Are nanodielectrics a “universal” panacea? The answer to that question is ...

Computational advertising is an emerging scientific sub-discipline, at the intersection of large scale search and text analysis, information retrieval, statistical modeling, machine learning, classification, optimization, and microeconomics. The central challenge of computational advertising is to find the "best match" between a given user in a given context and a suitable advertisement. The aim of this tutorial is to present ...

Single document summarization generates summary by extracting the representative sentences from the document. In this paper, we presented a novel technique for summarization of domain-specific text from a single web document that uses statistical and linguistic analysis on the text in a reference corpus and the web document. The proposed summarizer uses the combinational function of Sentence Weight (SW) ...

Conference: Language Resources and Evaluation - LREC, 2010]]>http://journalogy.net/Publication/13994402Mon, 20 May 2013 02:35:43 GMT13994402Leonhard Hennig, Thomas Strecker, Sascha Narr, Ernesto William De Luca, Sahin Albayrakview publication

Statistical approaches to document content modeling typically focus either on broad topics or on discourse-level subtopics of a text. We present an analysis of the performance of probabilistic topic models on the task of learning sentence-level topics that are similar to facts. The identification of sentential content with the same meaning is an important task in multi-document ...

This paper proposes an automatic method to generate an extractive summary of multiple Vietnamese documents which are related to a common topic by modeling text documents as weighted undirected graphs. It initially builds undirected graphs with vertices representing the sentences of documents and edges indicate the similarity between sentences. Then, by adopting PageRank algorithm, we can generate salient scores for ...

Text Summerization, is a topic that is related to the fields of philosophy and linguistics [2] are also included in the social sciences, so often sought by researchers in computer science who prefer something in the field of exact sciences. Interestingly, the Text Summerization applications, is a system that will make a summary or conclusion of tens or hundreds of ...

This paper describes an experimental method for automatic text genre recognition based on 45 statistical, lexical, syntactic, positional, and discursive parameters. The suggested method includes: (1) the development of software permitting heterogeneous parameters to be normalized and clustered using the k-means algorithm; (2) the verification of parameters; (3) the selection of the parameters that are the most significant for ...

In this paper, we will apply a recently proposed connectionist model, namely, the Graph Neural Network, for processing the graph formed by considering each sentence in a document as a node and the relationship between two sentences as an edge. Using commonly accepted evaluation protocols, the ROGUE toolkit, the technique was applied to two text summarization benchmarks, namely DUC–2001 ...

In this paper, we propose automatic categorization and summarization of documentaries using subtitles of videos. We propose two methods for video categorization. The first makes unsupervised categorization by applying natural language processing techniques on video subtitles and uses the WordNet lexical database and WordNet domains. The second has the same extraction steps but uses a learning module to categorize. Experiments ...

To date, few attempts have been made to develop and validate methods for au- tomatic evaluation of linguistic quality in text summarization. We present the first systematic assessment of several diverse classes of metrics designed to capture var- ious aspects of well-written text. We train and test linguistic quality models on con- secutive years of NIST evaluation data in ...

This paper describes an approach to Vietnamese text summarization, concentrated on the discourse structure of the text. Based on characteristics of Vietnamese, we propose rules for segmenting text into elementary discourse units (edus) and for recognizing discourse relations between textual spans. The score of an edu is computed based on the discourse tree. The edus with highest scores are chosen ...

Summarization is a brief and accurate representation of input text such that the output covers the most important concepts of the source in a condensed manner. Text Summarization is an emerging technique for understanding the main purpose of any kind of documents. To visualize a large text document within a short duration and small visible area like PDA screen, summarization ...

Most common feature selection techniques for document categorization are supervised and require lots of training data in order to accurately capture the descriptive and discriminative information from the defined categories. Considering that training sets are extremely small in many classification tasks, in this paper we explore the use of unsupervised extractive summarization as a feature selection technique for document categorization. ...

In the recent years, with the expansion of the Internet there has been tremendous growth in the volume of electronic text documents available information on the Web, which making difficulty for users to locate efficiently needed information. To facilitate efficient searching for information, research to summarize the general outline of a text document is essential. Moreover, as the information from ...

We present NegativeRank, a novel graph-based sentence ranking model to improve the diversity of focused summary by performing random walks over sentence graph with negative edge weights. Unlike the typical eigenvector centrality ranking, our method models the redundancy among sentence nodes as the negative edges. The negative edges can be thought of as the propagation of disapproval votes which ...

Sentence compression is a valuable task in the framework of text summarization. In this paper we compress sentences from news articles from Dutch and Flemish newspapers written in Dutch using an integer linear programming approach. We rely on the Alpino parser available for Dutch and on the Latent Words Language Model. We demonstrate that the integer linear programming approach yields ...

Journal: Journal of Applied Sciences, vol. 10, no. 3, pp. 166-173, 2010]]>http://journalogy.net/Publication/48702187Mon, 20 May 2013 02:35:29 GMT48702187Dongmei Ai, Yuchao Zheng, Dezheng Zhangview publication

Automatic summarization is a topic of common concern in computational linguistics and information science, since a computer system of text summarization is considered to be an effective means of processing information resources. A method of text summarization based on latent semantic indexing (LSI), which uses semantic indexing to calculate the sentence similarity, is proposed in this article. It improves the ...

XML Retrieval is becoming the focus study of the field of Information Retrieval and Database. Summarization of the results which come from the XML search engines will alleviate the read burden of user's. However, as the basis of this study, the construction of the query-oriented XML text summarization corpus has not yet received enough attention. In this paper, ...

The humanistic love and care has been increasing with the society progress in the contemporary era. There are more concerns for children's health care and in particular psychology all the more. The text summarizes the characteristics and defects of dental facilities, and inquires into the train of thought on the design for children with the idea, people oriented as ...

For dissolving the imperfection that traditional text summarization method is poor to express domain-specific document meanings, techniques called post-processing to summaries are studied. The post-processing includes: elimination of redundancy; adjustment to coarse summary by clustering document paragraphs; generalization of summary sentences; filling domain-specific knowledge by means of constructing knowledge base. Finally, experiments on clothing field documents ...