-
Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006)
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- Date
- 22. 7.2006 17:25:48
- Source
- Journal of the American Society for Information Science and Technology. 57(2006) no.6, S.740-752
-
Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016)
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- Date
- 22. 1.2016 12:29:41
- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.2, S.366-379
-
Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023)
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- Date
- 22. 1.2023 18:57:12
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.2, S.234-248
-
Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023)
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- Date
- 22. 6.2023 14:55:20
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.7, S.759-774
-
Goh, A.; Hui, S.C.: TES: a text extraction system (1996)
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- Date
- 26. 2.1997 10:22:43
-
Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996)
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- Date
- 6. 3.1997 16:22:15
-
Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996)
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- Source
- Information retrieval: new systems and current research. Proceedings of the 16th Research Colloquium of the British Computer Society Information Retrieval Specialist Group, Drymen, Scotland, 22-23 Mar 94. Ed.: R. Leon
-
Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007)
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- Abstract
- In recent years, there has been increased interest in topic-focused multi-document summarization. In this task, automatic summaries are produced in response to a specific information request, or topic, stated by the user. The system we have designed to accomplish this task comprises four main components: a generic extractive summarization system, a topic-focusing component, sentence simplification, and lexical expansion of topic words. This paper details each of these components, together with experiments designed to quantify their individual contributions. We include an analysis of our results on two large datasets commonly used to evaluate task-focused summarization, the DUC2005 and DUC2006 datasets, using automatic metrics. Additionally, we include an analysis of our results on the DUC2006 task according to human evaluation metrics. In the human evaluation of system summaries compared to human summaries, i.e., the Pyramid method, our system ranked first out of 22 systems in terms of overall mean Pyramid score; and in the human evaluation of summary responsiveness to the topic, our system ranked third out of 35 systems.
-
Marcu, D.: Automatic abstracting and summarization (2009)
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- Abstract
- After lying dormant for a few decades, the field of automated text summarization has experienced a tremendous resurgence of interest. Recently, many new algorithms and techniques have been proposed for identifying important information in single documents and document collections, and for mapping this information into grammatical, cohesive, and coherent abstracts. Since 1997, annual workshops, conferences, and large-scale comparative evaluations have provided a rich environment for exchanging ideas between researchers in Asia, Europe, and North America. This entry reviews the main developments in the field and provides a guiding map to those interested in understanding the strengths and weaknesses of an increasingly ubiquitous technology.
-
Lam, W.; Chan, K.; Radev, D.; Saggion, H.; Teufel, S.: Context-based generic cross-lingual retrieval of documents and automated summaries (2005)
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- Source
- Journal of the American Society for Information Science and Technology. 56(2005) no.2, S.129-139
-
Chen, H.-H.; Kuo, J.-J.; Huang, S.-J.; Lin, C.-J.; Wung, H.-C.: ¬A summarization system for Chinese news from multiple sources (2003)
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- Source
- Journal of the American Society for Information Science and technology. 54(2003) no.13, S.1224-1236
-
Sparck Jones, K.: Automatic summarising : the state of the art (2007)
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- Abstract
- This paper reviews research on automatic summarising in the last decade. This work has grown, stimulated by technology and by evaluation programmes. The paper uses several frameworks to organise the review, for summarising itself, for the factors affecting summarising, for systems, and for evaluation. The review examines the evaluation strategies applied to summarising, the issues they raise, and the major programmes. It considers the input, purpose and output factors investigated in recent summarising research, and discusses the classes of strategy, extractive and non-extractive, that have been explored, illustrating the range of systems built. The conclusions drawn are that automatic summarisation has made valuable progress, with useful applications, better evaluation, and more task understanding. But summarising systems are still poorly motivated in relation to the factors affecting them, and evaluation needs taking much further to engage with the purposes summaries are intended to serve and the contexts in which they are used.
-
Wang, W.; Hwang, D.: Abstraction Assistant : an automatic text abstraction system (2010)
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- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.9, S.1790-1799
-
Martinez-Romo, J.; Araujo, L.; Fernandez, A.D.: SemGraph : extracting keyphrases following a novel semantic graph-based approach (2016)
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- Source
- Journal of the Association for Information Science and Technology. 67(2016) no.1, S.71-82
-
Jones, S.; Paynter, G.W.: Automatic extractionof document keyphrases for use in digital libraries : evaluations and applications (2002)
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- Source
- Journal of the American Society for Information Science and technology. 53(2002) no.8, S.653-677
-
Ou, S.; Khoo, S.G.; Goh, D.H.: Automatic multidocument summarization of research abstracts : design and user evaluation (2007)
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- Source
- Journal of the American Society for Information Science and Technology. 58(2007) no.10, S.1419-1435
-
Yang, C.C.; Wang, F.L.: Hierarchical summarization of large documents (2008)
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- Source
- Journal of the American Society for Information Science and Technology. 59(2008) no.6, S.887-902
-
Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009)
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- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.10, S.2119-2131
-
Ouyang, Y.; Li, W.; Li, S.; Lu, Q.: Intertopic information mining for query-based summarization (2010)
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- Journal of the American Society for Information Science and Technology. 61(2010) no.5, S.1062-1072
-
Cai, X.; Li, W.: Enhancing sentence-level clustering with integrated and interactive frameworks for theme-based summarization (2011)
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- Source
- Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.2067-2082
