Search (12 results, page 1 of 1)

  • × theme_ss:"Automatisches Abstracting"
  1. Craven, T.C.: Presentation of repeated phrases in a computer-assisted abstracting tool kit (2001) 0.08
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  2. Craven, T.C.: ¬A phrase flipper for the assistance of writers of abstracts and other text (1995) 0.04
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    Abstract
    Describes computerized tools for computer assisted abstracting. FlipPhr is a Microsoft Windows application program that rearranges (flips) phrases or other expressions in accordance with rules in a grammar. The flipping may be invoked with a single keystroke from within various Windows application programs that allow cutting and pasting of text. The user may modify the grammar to provide for different kinds of flipping
  3. Craven, T.C.: ¬An experiment in the use of tools for computer-assisted abstracting (1996) 0.03
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  4. Abdi, A.; Idris, N.; Alguliev, R.M.; Aliguliyev, R.M.: Automatic summarization assessment through a combination of semantic and syntactic information for intelligent educational systems (2015) 0.03
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    Abstract
    Summary writing is a process for creating a short version of a source text. It can be used as a measure of understanding. As grading students' summaries is a very time-consuming task, computer-assisted assessment can help teachers perform the grading more effectively. Several techniques, such as BLEU, ROUGE, N-gram co-occurrence, Latent Semantic Analysis (LSA), LSA_Ngram and LSA_ERB, have been proposed to support the automatic assessment of students' summaries. Since these techniques are more suitable for long texts, their performance is not satisfactory for the evaluation of short summaries. This paper proposes a specialized method that works well in assessing short summaries. Our proposed method integrates the semantic relations between words, and their syntactic composition. As a result, the proposed method is able to obtain high accuracy and improve the performance compared with the current techniques. Experiments have displayed that it is to be preferred over the existing techniques. A summary evaluation system based on the proposed method has also been developed.
  5. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01
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    Date
    26. 2.1997 10:22:43
  6. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.01
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    Date
    6. 3.1997 16:22:15
  7. Jones, P.A.; Bradbeer, P.V.G.: Discovery of optimal weights in a concept selection system (1996) 0.01
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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
  8. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.00
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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.
  9. Wu, Y.-f.B.; Li, Q.; Bot, R.S.; Chen, X.: Finding nuggets in documents : a machine learning approach (2006) 0.00
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    Date
    22. 7.2006 17:25:48
  10. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.00
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    Date
    22. 1.2016 12:29:41
  11. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.00
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    Date
    22. 1.2023 18:57:12
  12. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.00
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    Date
    22. 6.2023 14:55:20