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  • × theme_ss:"Automatisches Abstracting"
  1. Uyttendaele, C.; Moens, M.-F.; Dumortier, J.: SALOMON: automatic abstracting of legal cases for effective access to court decisions (1998) 0.01 
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    Abstract
    The SALOMON project summarises Belgian criminal cases in order to improve access to the large number of existing and future cases. A double methodology was used when developing SALOMON: the cases are processed by employing additional knowledge to interpret structural patterns and features on the one hand and by way of occurrence statistics of index terms on the other. SALOMON performs an initial categorisation and structuring of the cases and subsequently extracts the most relevant text units of the alleged offences and of the opinion of the court. The SALOMON techniques do not themselves solve any legal questions, but they do guide the use effectively towards relevant texts
  2. Marcu, D.: Automatic abstracting and summarization (2009) 0.01 
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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.
  3. Pinto, M.: Abstracting/abstract adaptation to digital environments : research trends (2003) 0.01 
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    Abstract
    The technological revolution is affecting the structure, form and content of documents, reducing the effectiveness of traditional abstracts that, to some extent, are inadequate to the new documentary conditions. Aims to show the directions in which abstracting/abstracts can evolve to achieve the necessary adequacy in the new digital environments. Three researching trends are proposed: theoretical, methodological and pragmatic. Theoretically, there are some needs for expanding the document concept, reengineering abstracting and designing interdisciplinary models. Methodologically, the trend is toward the structuring, automating and qualifying of the abstracts. Pragmatically, abstracts networking, combined with alternative and complementary models, open a new and promising horizon. Automating, structuring and qualifying abstracting/abstract offer some short-term prospects for progress. Concludes that reengineering, networking and visualising would be middle-term fruitful areas of research toward the full adequacy of abstracting in the new electronic age.
  4. Brandow, R.; Mitze, K.; Rau, L.F.: Automatic condensation of electronic publications by sentence selection (1995) 0.01 
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    Information processing and management. 31(1995) no.5, S.675-685
  5. Sparck Jones, K.; Endres-Niggemeyer, B.: Introduction: automatic summarizing (1995) 0.01 
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    Information processing and management. 31(1995) no.5, S.625-630
  6. Automatic summarizing : introduction (1995) 0.01 
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    Information processing and management. 31(1995) no.5, S.625-630
  7. Over, P.; Dang, H.; Harman, D.: DUC in context (2007) 0.01 
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    Information processing and management. 43(2007) no.6, S.1506-1520
  8. Harabagiu, S.; Hickl, A.; Lacatusu, F.: Satisfying information needs with multi-document summaries (2007) 0.01 
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    Information processing and management. 43(2007) no.6, S.1619-1642
  9. Sjöbergh, J.: Older versions of the ROUGEeval summarization evaluation system were easier to fool (2007) 0.01 
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    Information processing and management. 43(2007) no.6, S.1500-1505
  10. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.01 
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    Abstract
    The UCLA Library reorganized its software developers into focused subteams with one, the Labs Team, dedicated to conducting experiments. In this article we describe our first attempt at conducting a software development experiment, in which we attempted to improve our digital library's search results with metadata from cloud-based image tagging services. We explore the findings and discuss the lessons learned from our first attempt at running an experiment.
  11. Soricut, R.; Marcu, D.: Abstractive headline generation using WIDL-expressions (2007) 0.01 
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    Information processing and management. 43(2007) no.6, S.1536-1548
  12. Ercan, G.; Cicekli, I.: Using lexical chains for keyword extraction (2007) 0.01 
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    Information processing and management. 43(2007) no.6, S.1705-1714
  13. Lee, J.-H.; Park, S.; Ahn, C.-M.; Kim, D.: Automatic generic document summarization based on non-negative matrix factorization (2009) 0.01 
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    Information processing and management. 45(2009) no.1, S.20-34
  14. Plaza, L.; Stevenson, M.; Díaz, A.: Resolving ambiguity in biomedical text to improve summarization (2012) 0.01 
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    Information processing and management. 48(2012) no.4, S.755-766
  15. Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016) 0.01 
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    Information processing and management. 52(2016) no.4, S.670-681
  16. Wang, S.; Koopman, R.: Embed first, then predict (2019) 0.01 
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    Abstract
    Automatic subject prediction is a desirable feature for modern digital library systems, as manual indexing can no longer cope with the rapid growth of digital collections. It is also desirable to be able to identify a small set of entities (e.g., authors, citations, bibliographic records) which are most relevant to a query. This gets more difficult when the amount of data increases dramatically. Data sparsity and model scalability are the major challenges to solving this type of extreme multilabel classification problem automatically. In this paper, we propose to address this problem in two steps: we first embed different types of entities into the same semantic space, where similarity could be computed easily; second, we propose a novel non-parametric method to identify the most relevant entities in addition to direct semantic similarities. We show how effectively this approach predicts even very specialised subjects, which are associated with few documents in the training set and are more problematic for a classifier.
  17. Maybury, M.T.: Generating summaries from event data (1995) 0.01 
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    Information processing and management. 31(1995) no.5, S.735-751
  18. Moens, M.-F.; Uyttendaele, C.: Automatic text structuring and categorization as a first step in summarizing legal cases (1997) 0.01 
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    Information processing and management. 33(1997) no.6, S.727-737
  19. Liang, S.-F.; Devlin, S.; Tait, J.: Investigating sentence weighting components for automatic summarisation (2007) 0.01 
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    Information processing and management. 43(2007) no.1, S.146-153
  20. Sparck Jones, K.: Automatic summarising : the state of the art (2007) 0.01 
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    Information processing and management. 43(2007) no.6, S.1449-1481

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