The Experts below are selected from a list of 12789 Experts worldwide ranked by ideXlab platform
Inderjeet Mani - One of the best experts on this subject based on the ideXlab platform.
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CIKM - Recent developments in Text Summarization
Proceedings of the tenth international conference on Information and knowledge management - CIKM'01, 2001Co-Authors: Inderjeet ManiAbstract:With the explosion in the quantity of on-line Text and multimedia information in recent years, demand for Text Summarization technology is growing. Increased pressure for technology advances is coming from users of the web, on-line information sources, and new mobile devices, as well as from the need for corporate knowledge management. Commercial companies are increasingly starting to offer Text Summarization capabilities, often bundled with information retrieval tools. In this paper, I will discuss the significance of some recent developments in Summarization technology.
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advances in automatic Text Summarization
Computational Linguistics, 1999Co-Authors: Inderjeet Mani, Mark T MayburyAbstract:From the Publisher: Text Summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user or task.. "Until now there has been no state-of-the-art collection of the most important writings in automatic Text Summarization. This book presents the key developments in the field in an integrated framework and suggests future research areas. The book is organized into six sections. Classical Approaches, Corpus-Based Approaches, Exploiting Discourse Structure, Knowledge-Rich Approaches, Evaluation Methods, and New Summarization Problem Areas.
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the tipster summac Text Summarization evaluation
Conference of the European Chapter of the Association for Computational Linguistics, 1999Co-Authors: Inderjeet Mani, David House, Gary L Klein, Lynette Hirschman, Therese Firmin, Beth SundheimAbstract:The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic Text Summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full Text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in F-score accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation of informative summaries.
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EACL - The TIPSTER SUMMAC Text Summarization Evaluation
Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics -, 1999Co-Authors: Inderjeet Mani, David House, Gary L Klein, Lynette Hirschman, Therese Firmin, Beth SundheimAbstract:The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic Text Summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full Text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in F-score accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation of informative summaries.
Luciano Favaro - One of the best experts on this subject based on the ideXlab platform.
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a conText based Text Summarization system
Document Analysis Systems, 2014Co-Authors: Rafael Ferreira, Frederico Luiz Goncalves De Freitas, Luciano De Souza Cabral, Rafael Dueire Lins, Rinaldo Lima, Gabriel Franca, Steven J Simske, Luciano FavaroAbstract:Text Summarization is the process of creating a shorter version of one or more Text documents. Automatic Text Summarization has become an important way of finding relevant information in large Text libraries or in the Internet. Extractive Text Summarization techniques select entire sentences from documents according to some criteria to form a summary. Sentence scoring is the technique most used for extractive Text Summarization, today. Depending on the conText, however, some techniques may yield better results than some others. This paper advocates the thesis that the quality of the summary obtained with combinations of sentence scoring methods depend on Text subject. Such hypothesis is evaluated using three different conTexts: news, blogs and articles. The results obtained show the validity of the hypothesis formulated and point at which techniques are more effective in each of those conTexts studied.
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Assessing sentence scoring techniques for extractive Text Summarization
Expert Systems with Applications, 2013Co-Authors: Rafael Ferreira, Luciano De Souza Cabral, Rafael Dueire Lins, Rinaldo Lima, Steven J Simske, Gabriel De França Pereira E Silva, Fred Freitas, George D. C. Cavalcanti, Luciano FavaroAbstract:Abstract Text Summarization is the process of automatically creating a shorter version of one or more Text documents. It is an important way of finding relevant information in large Text libraries or in the Internet. Essentially, Text Summarization techniques are classified as Extractive and Abstractive. Extractive techniques perform Text Summarization by selecting sentences of documents according to some criteria. Abstractive summaries attempt to improve the coherence among sentences by eliminating redundancies and clarifying the contest of sentences. In terms of extractive Summarization, sentence scoring is the technique most used for extractive Text Summarization. This paper describes and performs a quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature. Three different datasets (News, Blogs and Article conTexts) were evaluated. In addition, directions to improve the sentence extraction results obtained are suggested.
Sukhpreet Kaur - One of the best experts on this subject based on the ideXlab platform.
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Study of automatic Text Summarization approaches in different languages
Artificial Intelligence Review, 2021Co-Authors: Yogesh Kumar, Komalpreet Kaur, Sukhpreet KaurAbstract:Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing vast amount of information about it. It is really a difficult task to manually extract the useful information from them. To solve this problem, automatic Text Summarization systems are developed. Text Summarization is a process of extracting useful information from large documents and compressing them into short summary preserving all important content. This survey paper hand out a broad overview on the work done in the field of automatic Text Summarization in different languages using various Text Summarization approaches. The focal centre of this survey paper is to present the research done on Text Summarization on Indian languages such as, Hindi, Punjabi, Bengali, Malayalam, Kannada, Tamil, Marathi, Assamese, Konkani, Nepali, Odia, Sanskrit, Sindhi, Telugu and Gujarati and foreign languages such as Arabic, Chinese, Greek, Persian, Turkish, Spanish, Czeh, Rome, Urdu, Indonesia Bhasha and many more. This paper provides the knowledge and useful support to the beginner scientists in this research area by giving a concise view on various feature extraction methods and classification techniques required for different types of Text Summarization approaches applied on both Indian and non-Indian languages.
Beth Sundheim - One of the best experts on this subject based on the ideXlab platform.
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the tipster summac Text Summarization evaluation
Conference of the European Chapter of the Association for Computational Linguistics, 1999Co-Authors: Inderjeet Mani, David House, Gary L Klein, Lynette Hirschman, Therese Firmin, Beth SundheimAbstract:The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic Text Summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full Text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in F-score accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation of informative summaries.
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EACL - The TIPSTER SUMMAC Text Summarization Evaluation
Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics -, 1999Co-Authors: Inderjeet Mani, David House, Gary L Klein, Lynette Hirschman, Therese Firmin, Beth SundheimAbstract:The TIPSTER Text Summarization Evaluation (SUMMAC) has established definitively that automatic Text Summarization is very effective in relevance assessment tasks. Summaries as short as 17% of full Text length sped up decision-making by almost a factor of 2 with no statistically significant degradation in F-score accuracy. SUMMAC has also introduced a new intrinsic method for automated evaluation of informative summaries.
Vishal Gupta - One of the best experts on this subject based on the ideXlab platform.
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Recent automatic Text Summarization techniques: a survey
Artificial Intelligence Review, 2017Co-Authors: Mahak Gambhir, Vishal GuptaAbstract: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.
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preprocessing phase of punjabi language Text Summarization
International Conference on Information Systems, 2011Co-Authors: Vishal Gupta, Gurpreet Singh LehalAbstract:Punjabi Text Summarization is the process of condensing the source Punjabi Text into a shorter version, preserving its information content and overall meaning. It comprises two phases: 1) Pre Processing 2) Processing. Pre Processing is structured representation of the Punjabi Text. This paper concentrates on Pre processing phase of Punjabi Text Summarization. Various sub phases of pre processing are: Punjabi words boundary identification, Punjabi language stop words elimination, Punjabi language noun stemming, finding Common English Punjabi noun words, finding Punjabi language proper nouns, Punjabi sentence boundary identification, and identification of Punjabi language Cue phrase in a sentence.
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A Survey of Text Summarization Extractive Techniques
Journal of Emerging Technologies in Web Intelligence, 2010Co-Authors: Vishal Gupta, Gurpreet Singh LehalAbstract:Text Summarization is condensing the source Text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of Text. Text Summarization methods can be classified into extractive and Abstractive Summarization. An extractive Summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An Abstractive Summarization method consists of understanding the original Text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the Text and then to find the new concepts and expressions to best describe it by generating a new shorter Text that conveys the most important information from the original Text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented.
