As information is available in abundance for every topic on internet, condensing the important information in the form of summary would benefit a number of users. Hence, there is growing interest among the research community for developing new approaches to automatically summarize the text. Automatic text summarization system generates a summary, i.e. short length text that includes all the important information of the document. Since the advent of text summarization in 1950s, researchers have been trying to improve techniques for generating summaries so that machine generated summary matches with the human made summary. Summary can be generated through extractive as well as abstractive methods. Abstractive methods are highly complex as they need extensive natural language processing. Therefore, research community is focusing more on extractive summaries, trying to achieve more coherent and meaningful summaries. During a decade, several extractive approaches have been developed for automatic summary generation that implements a number of machine learning and optimization techniques. This paper presents a comprehensive survey of recent text summarization extractive approaches developed in the last decade. Their needs are identified and their advantages and disadvantages are listed in a comparative manner. A few abstractive and multilingual text summarization approaches are also covered. Summary evaluation is another challenging issue in this research field. Therefore, intrinsic as well as extrinsic both the methods of summary evaluation are described in detail along with text summarization evaluation conferences and workshops. Furthermore, evaluation results of extractive summarization approaches are presented on some shared DUC datasets. Finally this paper concludes with the discussion of useful future directions that can help researchers to identify areas where further research is needed.

Recent automatic text summarization techniques: a survey

Automatic Text Summarization (ATS) is becoming much more important because of the huge amount of textual content that grows exponentially on the Internet and the various archives of news articles, scientific papers, legal documents, etc. Manual text summarization consumes a lot of time, effort, cost, and even becomes impractical with the gigantic amount of textual content. Researchers have been trying to improve ATS techniques since the 1950s. ATS approaches are either extractive, abstractive, or hybrid. The extractive approach selects the most important sentences in the input document(s) then concatenates them to form the summary. The abstractive approach represents the input document(s) in an intermediate representation then generates the summary with sentences that are different than the original sentences. The hybrid approach combines both the extractive and abstractive approaches. Despite all the proposed methods, the generated summaries are still far away from the human-generated summaries. Most researches focus on the extractive approach. It is required to focus more on the abstractive and hybrid approaches. This research provides a comprehensive survey for the researchers by presenting the different aspects of ATS: approaches, methods, building blocks, techniques, datasets, evaluation methods, and future research directions.

Automatic text summarization: A comprehensive survey

Comprehensive Survey of Image Steganography: Techniques, Evaluations, and Trends in Future Research

This article surveys work on Unsupervised Learning of Morphology. We define Unsupervised Learning of Morphology as the problem of inducing a description (of some kind, even if only morpheme-segmentation) of how orthographic words are built up given only raw text data of a language. We briefly go through the history and motivation of the this problem. Next, over 200 items of work are listed with a brief characterization, and the most important ideas in the field are critically discussed. We summarize the achievements so far and give pointers for future developments.

/pdf/unsupervised-learning-of-morphology-4ibjeuf7oe.pdf

Unsupervised learning of morphology

While human tutors typically interact with students using spoken dialogue, most computer dialogue tutors are text-based. We have conducted 2 experiments comparing typed and spoken tutoring dialogues, one in a human-human scenario, and another in a human-computer scenario. In both experiments, we compared spoken versus typed tutoring for learning gains and time on task, and also measured the correlations of learning gains with dialogue features. Our main results are that changing the modality from text to speech caused large differences in the learning gains, time and superficial dialogue characteristics of human tutoring, but for computer tutoring it made less difference.

Spoken versus typed human and computer dialogue tutoring

Stemmers are used to convert inflected words into their root or stem. Stem does not necessarily correspond to linguistic root of a word. Stemming improve performance by reducing morphologically variants into same words. This paper presents an approach is to develop unsupervised Hindi stemmer. This paper focus on the development of an unsupervised stemmer for Hindi and evaluation of approach using manually segmented words. We evaluate our approach on 1000-1000 words randomly extracted words (only) from Hindi WordNet1 data base. The training data has been constructed by extracting 106403 words extracted from EMILLE2 corpus. The observed accuracy was found to be 89.9% after applying some heuristic measures. The F-score was 94.96%. As the algorithm does not require any language specific information, it can be applied to other Indian languages as well. We also evaluate the effect of stemmer in terms of reducing size of index for Hindi information retrieval task. The results have been compared with light weight stemmer [10] and UMass stemmer [17]. Test run shows that our stemmer outperforms both the stemmer.

An unsupervised Hindi stemmer with heuristic improvements

In this paper, a new robust steganography algorithm based on discrete cosine transform (DCT), Arnold transform and chaotic system is proposed. The chaotic system is used to generate a random sequence to be used for spreading data in the middle frequency band DCT coefficient of the cover image. The security is further enhanced by scrambling the secret data using Arnold Cat map before embedding. The recovery process is blind. A series of experiments is conducted to prove the security and robustness of the proposed algorithm. The experimental results demonstrate that the proposed algorithm achieves higher security and robustness against JPEG compression, addition of noise, low pass filtering and cropping attacks as compared to other existing algorithms for data hiding in the DCT domain.

A Security Enhanced Robust Steganography Algorithm for Data Hiding

This paper investigates the effects of stemming, stop word removal and size of context window on Hindi word sense disambiguation. The evaluation has been made on a manually created sense tagged corpus consisting of Hindi words (nouns). The sense definition has been obtained from Hindi WordNet, which is an important lexical resource for Hindi language developed at IIT Bombay. The maximum observed precision of 54.81% on 1248 test instances corresponds to the case when both stemming and stop words elimination has been performed. The % improvement in precision and recall is 9.24% and 12.68% over the baseline performance.

Evaluating effect of context window size, stemming and stop word removal on Hindi word sense disambiguation

Medical images are of high importance and patient data must be kept confidential. In this chapter, we discuss a new hybrid transform domain technique for medical image watermarking and provide a detailed analysis of existing image watermarking methods. The proposed method uses a combination of nonsubsampled contourlet transform (NSCT), discrete cosine transform (DCT) and singular value decomposition (SVD) to achieve high capacity, robustness and imperceptibility. This method is non blind which requires cover image in receiver to extract watermarked image. Cover and watermark images are pre-processed in order to ensure accurate extraction of watermark. In this approach, we have considered medical images as cover and electronic patient record (EPR) is used as secret message. EPR message is embedded into selected sub band of cover image with selected gain factor so that there should be a good trade off among imperceptibility, robustness and capacity. NSCT increases hiding capacity and is more resistant to geometrical attacks. The combination of NSCT with DCT and SVD enhanced the perceptual quality and security of watermarked image. Experimental demonstration proved that the proposed method provides high robustness against geometrical and signal processing attacks in terms of peak signal to noise ratio (PSNR) and correlation coefficient (CC).

SVD-DCT Based Medical Image Watermarking in NSCT Domain

Natural Language Processing and Information Retrieval is a textbook designed to meet the requirements of engineering students pursuing undergraduate and postgraduate programs in computer science and information technology The book attempts to bridge the gap between theory and practice and would also serve as a useful reference for professionals and researchers working on language-related projects Integrating two rapidly developing and popular research fields of language processing and information retrieval, the book provides an extensive coverage of various concepts and widely used techniques in these areas The text includes topics such as language modeling, lexical analysis, computational modeling, grammar and parsing, and semantic as well as knowledge-based analysis The statistical and semantic approaches are explained with examples from Hindi, English, and Urdu Besides presenting traditional applications of machine translation and natural language generation, the book discusses recent trends and practices of information retrieval, text summarization, and information extraction in sufficient detail Written in easy-to-understand and student-friendly style, the textbook also provides ample practical applications based on hands-on research experience wherever appropriate

http://www.gbv.de/dms/ilmenau/toc/584806043.PDF

Tanveer J. Siddiqui

Papers

An unsupervised Hindi stemmer with heuristic improvements

A Security Enhanced Robust Steganography Algorithm for Data Hiding

Evaluating effect of context window size, stemming and stop word removal on Hindi word sense disambiguation

SVD-DCT Based Medical Image Watermarking in NSCT Domain

Natural Language Processing and Information Retrieval