Journal articles: 'Automatic multi-document summarization'

1

Kumar. "Automatic Multi Document Summarization Approaches." Journal of Computer Science 8, no. 1 (January 1, 2012): 133–40. http://dx.doi.org/10.3844/jcssp.2012.133.140.

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Faguo Zhou. "Research on Chinese Multi-document Automatic Summarization Algorithms." International Journal of Advancements in Computing Technology 4, no. 23 (December 31, 2012): 43–49. http://dx.doi.org/10.4156/ijact.vol4.issue23.6.

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Diedrichsen, Elke. "Linguistic challenges in automatic summarization technology." Journal of Computer-Assisted Linguistic Research 1, no. 1 (June 26, 2017): 40. http://dx.doi.org/10.4995/jclr.2017.7787.

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Automatic summarization is a field of Natural Language Processing that is increasingly used in industry today. The goal of the summarization process is to create a summary of one document or a multiplicity of documents that will retain the sense and the most important aspects while reducing the length considerably, to a size that may be user-defined. One differentiates between extraction-based and abstraction-based summarization. In an extraction-based system, the words and sentences are copied out of the original source without any modification. An abstraction-based summary can compress, fuse or paraphrase sections of the source document. As of today, most summarization systems are extractive. Automatic document summarization technology presents interesting challenges for Natural Language Processing. It works on the basis of coreference resolution, discourse analysis, named entity recognition (NER), information extraction (IE), natural language understanding, topic segmentation and recognition, word segmentation and part-of-speech tagging. This study will overview some current approaches to the implementation of auto summarization technology and discuss the state of the art of the most important NLP tasks involved in them. We will pay particular attention to current methods of sentence extraction and compression for single and multi-document summarization, as these applications are based on theories of syntax and discourse and their implementation therefore requires a solid background in linguistics. Summarization technologies are also used for image collection summarization and video summarization, but the scope of this paper will be limited to document summarization.

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Dief, Nada A., Ali E. Al-Desouky, Amr Aly Eldin, and Asmaa M. El-Said. "An Adaptive Semantic Descriptive Model for Multi-Document Representation to Enhance Generic Summarization." International Journal of Software Engineering and Knowledge Engineering 27, no. 01 (February 2017): 23–48. http://dx.doi.org/10.1142/s0218194017500024.

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Due to the increasing accessibility of online data and the availability of thousands of documents on the Internet, it becomes very difficult for a human to review and analyze each document manually. The sheer size of such documents and data presents a significant challenge for users. Providing automatic summaries of specific topics helps the users to overcome this problem. Most of the current extractive multi-document summarization systems can successfully extract summary sentences; however, many limitations exist which include the degree of redundancy, inaccurate extraction of important sentences, low coverage and poor coherence among the selected sentences. This paper introduces an adaptive extractive multi-document generic (EMDG) methodology for automatic text summarization. The framework of this methodology relies on a novel approach for sentence similarity measure, a discriminative sentence selection method for sentence scoring and a reordering technique for the extracted sentences after removing the redundant ones. Extensive experiments are done on the summarization benchmark datasets DUC2005, DUC2006 and DUC2007. This proves that the proposed EMDG methodology is more effective than the current extractive multi-document summarization systems. Rouge evaluation for automatic summarization is used to validate the proposed EMDG methodology, and the experimental results showed that it is more effective and outperforms the baseline techniques, where the generated summary is characterized by high coverage and cohesion.

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Rahamat Basha, S., J. Keziya Rani, and J. J. C. Prasad Yadav. "A Novel Summarization-based Approach for Feature Reduction Enhancing Text Classification Accuracy." Engineering, Technology & Applied Science Research 9, no. 6 (December 1, 2019): 5001–5. http://dx.doi.org/10.48084/etasr.3173.

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Automatic summarization is the process of shortening one (in single document summarization) or multiple documents (in multi-document summarization). In this paper, a new feature selection method for the nearest neighbor classifier by summarizing the original training documents based on sentence importance measure is proposed. Our approach for single document summarization uses two measures for sentence similarity: the frequency of the terms in one sentence and the similarity of that sentence to other sentences. All sentences were ranked accordingly and the sentences with top ranks (with a threshold constraint) were selected for summarization. The summary of every document in the corpus is taken into a new document used for the summarization evaluation process.

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Kongara, Srinivasa Rao, Dasika Sree Rama Chandra Murthy, and Gangadhara Rao Kancherla. "An Automatic Text Summarization Method with the Concern of Covering Complete Formation." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 977–86. http://dx.doi.org/10.2174/2213275912666190716105347.

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Background: Text summarization is the process of generating a short description of the entire document which is more difficult to read. This method provides a convenient way of extracting the most useful information and a short summary of the documents. In the existing research work, this is focused by introducing the Fuzzy Rule-based Automated Summarization Method (FRASM). Existing work tends to have various limitations which might limit its applicability to the various real-world applications. The existing method is only suitable for the single document summarization where various applications such as research industries tend to summarize information from multiple documents. Methods: This paper proposed Multi-document Automated Summarization Method (MDASM) to introduce the summarization framework which would result in the accurate summarized outcome from the multiple documents. In this work, multi-document summarization is performed whereas in the existing system only single document summarization was performed. Initially document clustering is performed using modified k means cluster algorithm to group the similar kind of documents that provides the same meaning. This is identified by measuring the frequent term measurement. After clustering, pre-processing is performed by introducing the Hybrid TF-IDF and Singular value decomposition technique which would eliminate the irrelevant content and would result in the required content. Then sentence measurement is one by introducing the additional metrics namely Title measurement in addition to the existing work metrics to accurately retrieve the sentences with more similarity. Finally, a fuzzy rule system is applied to perform text summarization. Results: The overall evaluation of the research work is conducted in the MatLab simulation environment from which it is proved that the proposed research method ensures the optimal outcome than the existing research method in terms of accurate summarization. MDASM produces 89.28% increased accuracy, 89.28% increased precision, 89.36% increased recall value and 70% increased the f-measure value which performs better than FRASM. Conclusion: The summarization processes carried out in this work provides the accurate summarized outcome.

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CHALI, YLLIAS, and SADID A. HASAN. "Query-focused multi-document summarization: automatic data annotations and supervised learning approaches." Natural Language Engineering 18, no. 1 (April 7, 2011): 109–45. http://dx.doi.org/10.1017/s1351324911000167.

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AbstractIn this paper, we apply different supervised learning techniques to build query-focused multi-document summarization systems, where the task is to produce automatic summaries in response to a given query or specific information request stated by the user. A huge amount of labeled data is a prerequisite for supervised training. It is expensive and time-consuming when humans perform the labeling task manually. Automatic labeling can be a good remedy to this problem. We employ five different automatic annotation techniques to build extracts from human abstracts using ROUGE, Basic Element overlap, syntactic similarity measure, semantic similarity measure, and Extended String Subsequence Kernel. The supervised methods we use are Support Vector Machines, Conditional Random Fields, Hidden Markov Models, Maximum Entropy, and two ensemble-based approaches. During different experiments, we analyze the impact of automatic labeling methods on the performance of the applied supervised methods. To our knowledge, no other study has deeply investigated and compared the effects of using different automatic annotation techniques on different supervised learning approaches in the domain of query-focused multi-document summarization.

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Manju, K., S. David Peter, and Sumam Idicula. "A Framework for Generating Extractive Summary from Multiple Malayalam Documents." Information 12, no. 1 (January 18, 2021): 41. http://dx.doi.org/10.3390/info12010041.

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Automatic extractive text summarization retrieves a subset of data that represents most notable sentences in the entire document. In the era of digital explosion, which is mostly unstructured textual data, there is a demand for users to understand the huge amount of text in a short time; this demands the need for an automatic text summarizer. From summaries, the users get the idea of the entire content of the document and can decide whether to read the entire document or not. This work mainly focuses on generating a summary from multiple news documents. In this case, the summary helps to reduce the redundant news from the different newspapers. A multi-document summary is more challenging than a single-document summary since it has to solve the problem of overlapping information among sentences from different documents. Extractive text summarization yields the sensitive part of the document by neglecting the irrelevant and redundant sentences. In this paper, we propose a framework for extracting a summary from multiple documents in the Malayalam Language. Also, since the multi-document summarization data set is sparse, methods based on deep learning are difficult to apply. The proposed work discusses the performance of existing standard algorithms in multi-document summarization of the Malayalam Language. We propose a sentence extraction algorithm that selects the top ranked sentences with maximum diversity. The system is found to perform well in terms of precision, recall, and F-measure on multiple input documents.

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Fejer, Hamzah Noori, and Nazlia Omar. "Automatic Multi-Document Arabic Text Summarization Using Clustering and Keyphrase Extraction." Journal of Artificial Intelligence 8, no. 1 (December 15, 2014): 1–9. http://dx.doi.org/10.3923/jai.2015.1.9.

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Park, Sun, ByungRea Cha, and DongUn An. "Automatic Multi-document Summarization Based on Clustering and Nonnegative Matrix Factorization." IETE Technical Review 27, no. 2 (2010): 167. http://dx.doi.org/10.4103/0256-4602.60169.

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Ye, Feiyue, and Xinchen Xu. "Automatic Multi-Document Summarization Based on Keyword Density and Sentence-Word Graphs." Journal of Shanghai Jiaotong University (Science) 23, no. 4 (June 7, 2018): 584–92. http://dx.doi.org/10.1007/s12204-018-1957-2.

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Hewahi, Nabil M., and Kathrein Abu Kwaik. "Automatic Arabic Text Summarization System (AATSS) Based on Semantic Features Extraction." International Journal of Technology Diffusion 3, no. 2 (April 2012): 12–27. http://dx.doi.org/10.4018/jtd.2012040102.

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Recently, the need has increased for an effective and powerful tool to automatically summarize text. For English and European languages an intensive works have been done with high performance and nowadays they look forward to multi-document and multi-language summarization. However, Arabic language still suffers from the little attentions and research done in this filed. In this paper, we propose a model to automatically summarize Arabic text using text extraction. Various steps are involved in the approach: preprocessing text, extract set of features, classify sentence based on scoring method, ranking sentences and finally generate an extracted summary. The main difference between the proposed system and other Arabic summarization systems are the consideration of semantics, entity objects such as names and places, and similarity factors in our proposed system. The proposed system has been applied on news domain using a dataset osbtained from Local newspaper. Manual evaluation techniques are used to evaluate and test the system. The results obtained by the proposed method achieve 86.5% similarity between the system and human summarization. A comparative study between our proposed system and Sakhr Arabic online summarization system has been conducted. The results show that our proposed system outperforms Shakr system.

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Khodra, Masayu Leylia, Dwi Hendratmo Widyantoro, E. Aminudin Aziz, and Bambang Riyanto Trilaksono. "Automatic Tailored Multi-Paper Summarization based on Rhetorical Document Profile and Summary Specification." ITB Journal of Information and Communication Technology 6, no. 3 (December 2012): 220–39. http://dx.doi.org/10.5614/itbj.ict.2012.6.3.4.

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SARAVANAN, M., S. RAMAN, and B. RAVINDRAN. "A PROBABILISTIC APPROACH TO MULTI-DOCUMENT SUMMARIZATION FOR GENERATING A TILED SUMMARY." International Journal of Computational Intelligence and Applications 06, no. 02 (June 2006): 231–43. http://dx.doi.org/10.1142/s1469026806001976.

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Data availability is not a major issue at present times in view of the widespread use of Internet; however, information and knowledge availability are the issues. Due to data overload and time-critical nature of information need, automatic summarization of documents plays a significant role in information retrieval and text data mining. This paper discusses the design of a multi-document summarizer that uses Katz's K-mixture model for term distribution. The model helps in ranking the sentences by a modified term weight assignment. Highly ranked sentences are selected for the final summary. The sentences that are repetitive in nature are eliminated, and a tiled summary is produced. Our method avoids redundancy and produces a readable (even browsable) summary, which we refer to as an event-specific tiled summary. The system has been evaluated against the frequently occurring sentences in the summaries generated by a set of human subjects. Our system outperforms other auto-summarizers at different extraction levels of summarization with respect to the ideal summary, and is close to the ideal summary at 40% extraction level.

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Dias, Márcio De Souza, Ariani Di Felippo, Amanda Pontes Rassi, Paula Cristina Figueira Cardoso, Fernando Antônio Asevedo Nóbrega, and Thiago Alexandre Salgueiro Pardo. "An investigation of linguistic problems in automatic multi-document summaries / Uma investigação de problemas linguísticos em sumários automáticos multidocumento." REVISTA DE ESTUDOS DA LINGUAGEM 29, no. 2 (March 19, 2021): 859. http://dx.doi.org/10.17851/2237-2083.29.2.859-907.

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Abstract: Automatic summaries commonly present diverse linguistic problems that affect textual quality and thus their understanding by users. Few studies have tried to characterize such problems and their relation with the performance of the summarization systems. In this paper, we investigated the problems in multi-document extracts (i.e., summaries produced by concatenating several sentences taken exactly as they appear in the source texts) generated by systems for Brazilian Portuguese that have different approaches (i.e., superficial and deep) and performances (i.e., baseline and state-of-the art methods). For that, we first reviewed the main characterization studies, resulting in a typology of linguistic problems more suitable for multi-document summarization. Then, we manually annotated a corpus of automatic multi-document extracts in Portuguese based on the typology, which showed that some of linguistic problems are significantly more recurrent than others. Thus, this corpus annotation may support research on linguistic problems detection and correction for summary improvement, allowing the production of automatic summaries that are not only informative (i.e., they convey the content of the source material), but also linguistically well structured.Keywords: automatic summarization; multi-document summary; linguistic problem; corpus annotation.Resumo: Sumários automáticos geralmente apresentam vários problemas linguísticos que afetam a sua qualidade textual e, consequentemente, sua compreensão pelos usuários. Alguns trabalhos caracterizam tais problemas e os relacionam ao desempenho dos sistemas de sumarização. Neste artigo, investigaram-se os problemas em extratos (isto é, sumários produzidos pela concatenação de sentenças extraídas na íntegra dos textos-fonte) multidocumento em Português do Brasil gerados por sistemas que apresentam diferentes abordagens (isto é, superficial e profunda) e desempenho (isto é, métodos baseline e do estado-da-arte). Para tanto, as principais caracterizações dos problemas linguísticos em sumários automáticos foram investigadas, resultando em uma tipologia mais adequada à sumarização multidocumento. Em seguida, anotou-se manualmente um corpus de extratos com base na tipologia, evidenciando que alguns tipos de problemas são significativamente mais recorrentes que outros. Assim, essa anotação gera subsídios para as tarefas automáticas de detecção e correção de problemas linguísticos com vistas à produção de sumários automáticos não só mais informativos (isto é, que cobrem o conteúdo do material de origem), como também linguisticamente bem-estruturados.Palavras-chave: sumarização automática; sumário multidocumento; problema linguístico; anotação de corpus.

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16

Sanchez-Gomez, Jesus M., Miguel A. Vega-Rodríguez, and Carlos J. Pérez. "Comparison of automatic methods for reducing the Pareto front to a single solution applied to multi-document text summarization." Knowledge-Based Systems 174 (June 2019): 123–36. http://dx.doi.org/10.1016/j.knosys.2019.03.002.

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17

Fu, Xiyan, Jun Wang, Jinghan Zhang, Jinmao Wei, and Zhenglu Yang. "Document Summarization with VHTM: Variational Hierarchical Topic-Aware Mechanism." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7740–47. http://dx.doi.org/10.1609/aaai.v34i05.6277.

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Automatic text summarization focuses on distilling summary information from texts. This research field has been considerably explored over the past decades because of its significant role in many natural language processing tasks; however, two challenging issues block its further development: (1) how to yield a summarization model embedding topic inference rather than extending with a pre-trained one and (2) how to merge the latent topics into diverse granularity levels. In this study, we propose a variational hierarchical model to holistically address both issues, dubbed VHTM. Different from the previous work assisted by a pre-trained single-grained topic model, VHTM is the first attempt to jointly accomplish summarization with topic inference via variational encoder-decoder and merge topics into multi-grained levels through topic embedding and attention. Comprehensive experiments validate the superior performance of VHTM compared with the baselines, accompanying with semantically consistent topics.

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18

Marom, Yuval, and Ingrid Zukerman. "An Empirical Study of Corpus-Based Response Automation Methods for an E-mail-Based Help-Desk Domain." Computational Linguistics 35, no. 4 (December 2009): 597–635. http://dx.doi.org/10.1162/coli.2009.35.4.35404.

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This article presents an investigation of corpus-based methods for the automation of help-desk e-mail responses. Specifically, we investigate this problem along two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. We consider two information-gathering techniques (retrieval and prediction) applied to information represented at two levels of granularity (document-level and sentence-level). Document-level methods correspond to the reuse of an existing response e-mail to address new requests. Sentence-level methods correspond to applying extractive multi-document summarization techniques to collate units of information from more than one e-mail. Evaluation of the performance of the different methods shows that in combination they are able to successfully automate the generation of responses for a substantial portion of e-mail requests in our corpus. We also investigate a meta-selection process that learns to choose one method to address a new inquiry e-mail, thus providing a unified response automation solution.

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K K A, Abdullah, Robert A B C, and Adeyemo A B. "August 2016 VOLUME 5, ISSUE 8, AUGUST 2016 5th Generation Wi-Fi Shatha Ghazal, Raina S Alkhlailah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5801 ECG Arrhythmia Detection Using Choi-Williams Time-Frequency Distribution and Artificial Neural Network Sanjit K. Dash, G. Sasibhushana Rao Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5802 Data Security using RSA Algorithm in Cloud Computing Santosh Kumar Singh, Dr. P.K. Manjhi, Dr. R.K. Tiwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5803 Detection Algorithms in Medical Imaging Priyanka Pareek, Pankaj Dalal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5804 A Review Study on the CPU Scheduling Algorithms Shweta Jain, Dr. Saurabh Jain Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5805 Healthcare Biosensors - A Paradigm Shift To Wireless Technology Taha Mukhtar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5806 Congestion Control for Peer to Peer Application using Random Early Detection Algorithm Sonam Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5807 Quantitative and Qualitative Analysis of Milk Parameters using Arduino Controller Y.R. Bhamare, M.B. Matsagar, C.G. Dighavkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5808 Ardunio Based Security and Safety using GSM as Fault Alert System for BTS (Base Transceiver Station) Umeshwari Khot, Prof. Venkat N. Ghodke Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5809 Automatic Single and Multi Topic Summarization and Evolution to Generate Timeline Mrs. V. Meenakshi, Ms. S. Jeyanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5810 Data Hiding in Encrypted HEVC/AVC Video Streams Saltanat Shaikh, Prof. Shahzia Sayyad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5811 A Study of Imbalanced Classification Problem P. Rajeshwari, D. Maheshwari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5812 Design of PTL based Area Efficient and Low Power 4-bit ALU Saraabu Narendra Achari, Mr. C. Pakkiraiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5813 The Design of Driver Safety Awareness and Assistance System through Sleep Activated and Auto Brake System for Vehicle Control D. Sivabalaselvamani, Dr. A. Tamilarasi, L. Rahunathan and A.S. Harishankher Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5814 Parameters Selection, Applications & Convergence Analysis of PSO Algorithms Sachin Kumar, Mr. N.K. Gupta Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5815 Effective Pattern Deploying Model for the Document Restructuring and Classification Niketa, Jharna Chopra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5816 Cataloging Telugu Sentences by Hidden Morkov Techniques V. Suresh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5817 Biometrics for Cell Phone Safety Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5818 Digital Image Watermarking using Modified DWT&DCT Combination and Bi Linear Interpolation Yannam .Nagarjuna, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5819 Comparative Study and Analysis on the Techniques of Web Mining Dipika Sahu, Yamini Chouhan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5820 A Review of MIL-STD-1553 Bus Trends and Future K. Padmanabham, Prabhakar Kanugo, Dr. K. Nagabhushan Raju, M. Chandrashekar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5821 Design of QPSK Digital Modulation Scheme Using Turbo Codes for an Air Borne System D. Sai Brunda, B. Geetha Rani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5822 An Efficient Locally Weighted Spectral Cluster for Automatic Image Segmentation Vishnu Priya M, J Santhosh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5823 An Efficient Sliding Window Based Micro Cluster Over Data Streams Nancy Mary, A. Venugopal Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5824 Comparative Analysis of Traditional Frequency Reuse Techniques in LTE Network Neelam Rani, Dr. Sanjeev Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5825 Score Level Integration of Fingerprint and Hand Geometry Biometrics Jyoti Tiwari, Santosh Kumar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5826 CHARM: Intelligently Cost and Bandwidth Detection for FTP Servers using Heuristic Algorithm Shiva Urolagin Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5827 Image Enhancement Using Modified Exposure Based Histogram SK. Nasreen, N. Anupama Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5828 Human Gesture Based Recognition and Classification Using MATLAB Suman, Er. Kapil Sirohi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5829 Image Denoising- A Novel Approach Dipali D. Sathe, Prof. K.N. Barbole Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5830 Design of Low Pass Digital FIR Filter Using Nature Inspired Technique Nisha Rani, Balraj Singh, Darshan Singh Sidhu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5831 Issues and Challenges in Software Quality Assurance Himangi, Surender singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5832 Hybridization of GSA and AFSA to Detect Black Hole Attack in Wireless Sensor Network Soni Rani, Charanjit Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5833 Reversible Watermarking Technique for Data Hiding, Accurate Tamper Detection in ROI and Exact Recovery of ROI Y. Usha Madhuri, K. Chaitanya Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5834 Fault Tolerance and Concurrency Control in Heterogeneous Distributed Database Systems Sagar Patel, Meghna Burli, Nidhi Shah, Prof. (Mrs.) Vinaya Sawant Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5835 Collection of Offline Tamil Handwriting Samples and Database Creation D. Rajalakshmi, Dr. S.K. Jayanthi Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5836 Overview of Renewable Energy in Maharashtra Mr. Sagar P. Thombare, Mr. Vishal Gunjal, Miss. Snehal Bhandarkar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5837 Comparative Analysis of Efficient Image Steganographic Technique with the 2-bit LSB Algorithm for Color Images K. S. Sadasiva Rao, Dr A. Damodaram Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5838 An Efficient Reverse Converter Design for Five Moduli Set RNS Y. Ayyavaru Reddy, B. Sekhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5839 VLSI Design of Area Efficient High Performance SPMV Accelerator using VBW-CBQCSR Scheme N. Narasimharao, A. Mallaiah Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5840 Customer Retention of MCDR using 3SCDM Approaches Suban Ravichandran, Chandrasekaran Ramasamy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5841 User Privacy and Data Trustworthiness in Mobile Crowd Sensing Ms. T. Sharadha, Dr. R. Vijaya Bhanu Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5842 A Safe Anti-Conspiracy Data Model For Changing Groups in Cloud G. Ajay Kumar, Devaraj Verma C Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5843 Scope and Adoption of M-Commerce in India Anurag Mishra, Sanjay Medhavi, Khan Shah Mohd, P.C. Mishra Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5844 A Secure Data Hiding Scheme For Color Image Mrs. S.A. Bhavani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5845 A Study of Different Content Based Image Retrieval Techniques C. Gururaj, D. Jayadevappa, Satish Tunga Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5846 Cache Management for Big Data Applications: Survey Kiran Grover, Surender Singh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5847 Survey on Energy Efficient Protocols and Challenges in IOT Syeda Butool Fatima, Sayyada Fahmeeda Sultana, Sadiya Ansari Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5848 Educational Data Mining For Evaluating Students Performance Sampreethi P.K, VR. Nagarajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5849 Iterative Pareto Principle for Software Test Case Prioritization Manas Kumar Yogi, G. Vijay Kumar, D. Uma Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5850 Localization Techniques in Wireless Sensor Networks: A Review Abhishek Kumar, Deepak Prashar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5851 Ensemble Averaging Filter for Noise Reduction Tom Thomas Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5852 Survey Paper on Get My Route Application Shubham A. Purohit, Tushar R. Khandare, Prof. Swapnil V. Deshmukh Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5853 Design and Implementation of Smart Car with Self-Navigation and Self-Parking Systems using Sensors and RFID Technology Madhuri M. Bijamwar, Prof. S.G. Kole, Prof. S.S. Savkare Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5854 Comparison Study of Induction Motor Drives using Microcontroller and FPGA Sooraj M S, Sreerag K T V Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5855 A Survey on Text Categorization Senthil Kumar B, Bhavitha Varma E Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5856 Multirate Signal Reconstruction Using Two Channel Orthogonal Filter Bank Sijo Thomas, Darsana P Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5857 The Multi-keyword Synonym Search for Encrypted Cloud Data Using Clustering Method Monika Rani H G, Varshini Vidyadhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5858 A Review on Various Speech Enhancement Techniques Alugonda Rajani, Soundarya .S.V.S Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5859 A Survey on Various Spoofing Attacks and Image Fusion Techniques Pravallika .P, Dr. K. Satya Prasad Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5860 Non-Invasive Vein Detection using Infra-red Rays Aradhana Singh, Dr. S.C. Prasanna Kumar, Dr. B.G. Sudershan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5861 Boundary-Polygons for Minutiae based Fingerprinst Recognition Kusha Maharshi, Prashant Sahai Saxena Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5862 Image Forgery Detection on Digital Images Nimi Susan Saji, Ranjitha Rajan Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5863 Enhancing Information Security in Big Data Renu Kesharwani Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5864 Secure Multi-Owner Data Sharing for Dynamic Groups in Cloud Ms. Nilophar M. Masuldar, Prof. V. P. Kshirsagar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5865 Compact Microstrip Octagonal Slot Antenna for Wireless Communication Applications Thasneem .H, Midhun Joy Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5866 ‘Aquarius’- Smart IOT Technology for Water Level Monitoring System Prof. A. M. Jagtap, Bhaldar Saniya Sikandar, Shinde Sharmila Shivaji, Khalate Vrushali Pramod, Nirmal Kalyani Sarangdhar Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5867 Future of Results in Select Search Engine Peerzada Mohammad Iqbal, Dr. Abdul Majid Baba, Aasim Bashir Abstract | PDF with Text | DOI: 10.17148/IJARCCE.2016.5868 Semantic Indexing Techniques on Information Retrieval of Web Content." IJARCCE 5, no. 8 (August 30, 2016): 347–52. http://dx.doi.org/10.17148/ijarcce.2016.5869.

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"Semantic Similarity Based Automatic Document Summarization Method." International Journal of Engineering and Advanced Technology 8, no. 6 (August 30, 2019): 2516–22. http://dx.doi.org/10.35940/ijeat.f8566.088619.

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Document summarization is the process of generating the summary of the documents gathered from the web sources. It reduces the burden of web readers by reducing the necessity of reading the entire document contents by generating the short summary. In our previous research work this is performed by introducing the method namely Noun weight based Automated Multi-Document Summarization method (NW-AMDSM). However the previous research work doesn’t concentrate on the semantic similarity which might reduce the accuracy of the summarization outcome. This is resolved in the proposed research method by introducing the method namely Semantic Similarity based Automatic Document Summarization Method (SS-ADSM). In this research work, multi document grouping is done is based on semantic similarity computation, thus the document with similar contents can be grouped more accurately. Here the semantic similarity computation is performed with the help of word net analyzer. The document grouping is done by introducing the modified FCM clustering algorithm. Finally hybrid neuro fuzzy genetic algorithm is introduced to perform the automatic summarization. The numerical analysis of the proposed research method is conducted in the matlab simulation environment and compared with other research methods in terms various performance metrics. The simulation analysis proved proposed method tends to have better performance in terms of increased accuracy of document summarization outcome.

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"Automatic Summarization of Textual Document." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (November 10, 2019): 2486–91. http://dx.doi.org/10.35940/ijitee.a4400.119119.

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In today world, there is a huge amount of information is growing every day on the internet and from many other sources and there is lots of textual information in it. To find out the relevant information from this large amount of data, we need an automatic mechanism that will extract the useful data. Such automatic systems are automatic summarization systems. They categorized into extractive and abstractive summarization system. Extractive summarization systems select the important sentences directly from the large document and put into summary whereas abstractive methods understand semantic meaning of the document by linguistic method to interpret and examine the text. In the purposed method, a statistical approach is used where multiple criterions or features are discussed to calculate the score for every sentence and then SIR (Susceptible Infected Recovered) model is used to compute the dynamic weight for every feature. After dynamic weight computation, weighted TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) is used for multi-criterion analysis and aggregation. This method is fully implemented and integrated for automated textual document summarization system.

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Mohammed, Dheyaa Abdulameer, and Nasreen J. Kadhim. "Extractive Multi-Document Summarization Model Based On Different Integrations of Double Similarity Measures." Iraqi Journal of Science, June 27, 2020, 1498–511. http://dx.doi.org/10.24996/ijs.2020.61.6.30.

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Currently, the prominence of automatic multi document summarization task belongs to the information rapid increasing on the Internet. Automatic document summarization technology is progressing and may offer a solution to the problem of information overload. Automatic text summarization system has the challenge of producing a high quality summary. In this study, the design of generic text summarization model based on sentence extraction has been redirected into a more semantic measure reflecting individually the two significant objectives: content coverage and diversity when generating summaries from multiple documents as an explicit optimization model. The proposed two models have been then coupled and defined as a single-objective optimization problem. Also, for improving the performance of the proposed model, different integrations concerning two similarity measures have been introduced and applied to the proposed model along with the single similarity measures that are based on using Cosine, Dice and similarity measures for measuring text similarity. For solving the proposed model, Genetic Algorithm (GA) has been used. Document sets supplied by Document Understanding Conference 2002 ( ) have been used for the proposed system as an evaluation dataset. Also, as an evaluation metric, Recall-Oriented Understudy for Gisting Evaluation ( ) toolkit has been used for performance evaluation of the proposed method. Experimental results have illustrated the positive impact of measuring text similarity using double integration of similarity measures against single similarity measure when applied to the proposed model wherein the best performance in terms of and has been recorded for the integration of Cosine similarity and similarity.

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Lee, Eva K., and Karan Uppal. "CERC: an interactive content extraction, recognition, and construction tool for clinical and biomedical text." BMC Medical Informatics and Decision Making 20, S14 (December 2020). http://dx.doi.org/10.1186/s12911-020-01330-8.

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Abstract Background Automated summarization of scientific literature and patient records is essential for enhancing clinical decision-making and facilitating precision medicine. Most existing summarization methods are based on single indicators of relevance, offer limited capabilities for information visualization, and do not account for user specific interests. In this work, we develop an interactive content extraction, recognition, and construction system (CERC) that combines machine learning and visualization techniques with domain knowledge for highlighting and extracting salient information from clinical and biomedical text. Methods A novel sentence-ranking framework multi indicator text summarization, MINTS, is developed for extractive summarization. MINTS uses random forests and multiple indicators of importance for relevance evaluation and ranking of sentences. Indicative summarization is performed using weighted term frequency-inverse document frequency scores of over-represented domain-specific terms. A controlled vocabulary dictionary generated using MeSH, SNOMED-CT, and PubTator is used for determining relevant terms. 35 full-text CRAFT articles were used as the training set. The performance of the MINTS algorithm is evaluated on a test set consisting of the remaining 32 full-text CRAFT articles and 30 clinical case reports using the ROUGE toolkit. Results The random forests model classified sentences as “good” or “bad” with 87.5% accuracy on the test set. Summarization results from the MINTS algorithm achieved higher ROUGE-1, ROUGE-2, and ROUGE-SU4 scores when compared to methods based on single indicators such as term frequency distribution, position, eigenvector centrality (LexRank), and random selection, p < 0.01. The automatic language translator and the customizable information extraction and pre-processing pipeline for EHR demonstrate that CERC can readily be incorporated within clinical decision support systems to improve quality of care and assist in data-driven and evidence-based informed decision making for direct patient care. Conclusions We have developed a web-based summarization and visualization tool, CERC (https://newton.isye.gatech.edu/CERC1/), for extracting salient information from clinical and biomedical text. The system ranks sentences by relevance and includes features that can facilitate early detection of medical risks in a clinical setting. The interactive interface allows users to filter content and edit/save summaries. The evaluation results on two test corpuses show that the newly developed MINTS algorithm outperforms methods based on single characteristics of importance.

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Bidoki, M., M. Fakhrahmad, and M. R. Moosavi. "Text Summarization as a Multi-objective Optimization Task: Applying Harmony Search to Extractive Multi-Document Summarization." Computer Journal, December 3, 2020. http://dx.doi.org/10.1093/comjnl/bxaa139.

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Abstract Today, automated extractive text summarization is one of the most common techniques for organizing information. In extractive summarization, the most appropriate sentences are selected from the text and build a representative summary. Therefore, probing for the best sentences is a fundamental task. This paper has coped with extractive summarization as a multi-objective optimization problem and proposed a language-independent, semantic-aware approach that applies the harmony search algorithm to generate appropriate multi-document summaries. It learns the objective function from an extra set of reference summaries and then generates the best summaries according to the trained function. The system also performs some supplementary activities for better achievements. It expands the sentences by using an inventive approach that aims at tuning conceptual densities in the sentences towards important topics. Furthermore, we introduced an innovative clustering method for identifying important topics and reducing redundancies. A sentence placement policy based on the Hamiltonian shortest path was introduced for producing readable summaries. The experiments were conducted on DUC2002, DUC2006 and DUC2007 datasets. Experimental results showed that the proposed framework could assist the summarization process and yield better performance. Also, it was able to generally outperform other cited summarizer systems.

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Journal articles on the topic 'Automatic multi-document summarization'

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