Search (33 results, page 1 of 2)

  • × theme_ss:"Automatisches Abstracting"
  1. Robin, J.; McKeown, K.: Empirically designing and evaluating a new revision-based model for summary generation (1996) 0.04
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    Date
    6. 3.1997 16:22:15
    Source
    Artificial intelligence. 85(1996) nos.1/2, S.135-179
  2. Kim, H.H.; Kim, Y.H.: Generic speech summarization of transcribed lecture videos : using tags and their semantic relations (2016) 0.03
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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
  3. Oh, H.; Nam, S.; Zhu, Y.: Structured abstract summarization of scientific articles : summarization using full-text section information (2023) 0.03
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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
  4. Goh, A.; Hui, S.C.: TES: a text extraction system (1996) 0.01
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    Date
    26. 2.1997 10:22:43
  5. 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
  6. Craven, T.C.: Presentation of repeated phrases in a computer-assisted abstracting tool kit (2001) 0.01
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    Source
    Information processing and management. 37(2001) no.2, S.221-230
  7. Bateman, J.; Teich, E.: Selective information presentation in an integrated publication system : an application of genre-driven text generation (1995) 0.01
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    Date
    2. 1.1996 14:46:02
  8. Vanderwende, L.; Suzuki, H.; Brockett, J.M.; Nenkova, A.: Beyond SumBasic : task-focused summarization with sentence simplification and lexical expansion (2007) 0.01
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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) 0.01
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    Date
    22. 7.2006 17:25:48
  10. Jiang, Y.; Meng, R.; Huang, Y.; Lu, W.; Liu, J.: Generating keyphrases for readers : a controllable keyphrase generation framework (2023) 0.01
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    Date
    22. 6.2023 14:55:20
  11. McKeown, K.; Robin, J.; Kukich, K.: Generating concise natural language summaries (1995) 0.01
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    Date
    2. 1.1996 14:46:02
  12. Salton, G.: Automatic text structuring and summarization (1997) 0.01
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    Abstract
    Applies the ideas from the automatic link generation research to automatic text summarisation. Using techniques for inter-document link generation, generates intra-document links between passages of a document. Based on the intra-document linkage pattern of a text, characterises the structure of the text. Applies the knowledge of text structure to do automatic text summarisation by passage extraction. Evaluates a set of 50 summaries generated using these techniques by comparing the to paragraph extracts constructed by humans. The automatic summarisation methods perform well, especially in view of the fact that the summaries generates by 2 humans for the same article are surprisingly dissimilar
    Source
    Information processing and management. 33(1997) no.2, S.193-207
  13. Goh, A.; Hui, S.C.; Chan, S.K.: ¬A text extraction system for news reports (1996) 0.01
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    Abstract
    Describes the design and implementation of a text extraction tool, NEWS_EXT, which aztomatically produces summaries from news reports by extracting sentences to form indicative abstracts. Selection of sentences is based on sentence importance, measured by means of sentence scoring or simple linguistic analysis of sentence structure. Tests were conducted on 4 approaches for the functioning of the NEWS_EXT system; extraction by keyword frequency; extraction by title keywords; extraction by location; and extraction by indicative phrase. Reports results of a study to compare the results of the application of NEWS_EXT with manually produced extracts; using relevance as the criterion for effectiveness. 48 newspaper articles were assessed (The Straits Times, International Herald Tribune, Asian Wall Street Journal, and Financial Times). The evaluation was conducted in 2 stages: stage 1 involving abstracts produced manually by 2 human experts; stage 2 involving the generation of abstracts using NEWS_EXT. Results of each of the 4 approaches were compared with the human produced abstracts, where the title and location approaches were found to give the best results for both local and foreign news. Reports plans to refine and enhance NEWS_EXT and incorporate it as a module within a larger newspaper clipping system
  14. Craven, T.C.: ¬A computer-aided abstracting tool kit (1993) 0.01
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    Source
    Canadian journal of information and library science. 18(1993) no.2, S.20-31
  15. Brandow, R.; Mitze, K.; Rau, L.F.: Automatic condensation of electronic publications by sentence selection (1995) 0.01
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    Date
    2. 1.1996 14:46:02
  16. Deutsche Patentdatenbank mit maschinellen Abstract-Übersetzungen (2005) 0.01
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    Source
    Password. 2005, H.2, S.32
  17. Automatic summarizing : introduction (1995) 0.01
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    Date
    2. 1.1996 14:46:02
  18. Harabagiu, S.; Hickl, A.; Lacatusu, F.: Satisfying information needs with multi-document summaries (2007) 0.01
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    Abstract
    Generating summaries that meet the information needs of a user relies on (1) several forms of question decomposition; (2) different summarization approaches; and (3) textual inference for combining the summarization strategies. This novel framework for summarization has the advantage of producing highly responsive summaries, as indicated by the evaluation results.
  19. Endres-Niggemeyer, B.; Maier, E.; Sigel, A.: How to implement a naturalistic model of abstracting : four core working steps of an expert abstractor (1995) 0.01
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    Date
    2. 1.1996 14:46:02
  20. Xiong, S.; Ji, D.: Query-focused multi-document summarization using hypergraph-based ranking (2016) 0.01
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    Date
    2. 6.2016 19:57:51