Dergiler / Istanbul University Journal of Electrical and Electronics Engineering / 2004 / Cilt: 4 - Sayı: 2

ARG: A tool for automatic report generation

Sayfa
1101–1109
Yayın Tarihi
2022-10-18
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Basically, we propose a two-phase algorithm in which the paragraphs in the documents are first classified according to given topics and then each topic is summarized to constitute the automatically generated report.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:The expansion of on-line text with the rapid growth of the Internet imposes utilizing Data Mining techniques to reveal the information embedded in these documents.
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Temel Bulgular & Veri:In this work, we attempt to integrate these two techniques to help the user to compile and extract the information that is needed.
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Sonuç & Katkı:Basically, we propose a two-phase algorithm in which the paragraphs in the documents are first classified according to given topics and then each topic is summarized to constitute the automatically generated report.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

The expansion of on-line text with the rapid growth of the Internet imposes utilizing Data Mining techniques to reveal the information embedded in these documents. Therefore text classification and text summarization are two of the most important application areas. In this work, we attempt to integrate these two techniques to help the user to compile and extract the information that is needed. Basically, we propose a two-phase algorithm in which the paragraphs in the documents are first classified according to given topics and then each topic is summarized to constitute the automatically generated report.

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Bu Makaleye Atıf Yap

KARAKAYA, K. M., GÜVENİR, H. A. (2022). ARG: A tool for automatic report generation. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 4 - Sayı: 2, 1101–1109. https://ajindex.com/articles/543879

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Kaynakça (13)

  1. Apte,C., Damerau,F., and Weiss,S., Towards language independent automated learning of text categorization models, In Proceedings of the 17th Annual ACM/SIGIR conference, 1994
  2. Apte,C., Damerau,F., and Weiss,S., Text mining with decision rules and decision trees, In Proceedings of the Conference on Automated Learning and Discorery, Workshop 6: Learning from Text and the Web, 1998
  3. Baker,L.D., Mccallum,A.K., Distributional clustering of words for text categorization, In Proceedings of the 21th Ann Int ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98), pages 96-103, 1998
  4. CNN Europe Web site, http://europe.cnn.com/, 2001
  5. Cohen,William W., Text categorization and relational learning, In The Twelfth International Conference on Machine Learning (ICML'95), Morgan Kaufmann, 1995
  6. Cohen,William W., Singer,Yoram, Context-sensitive learning methods for text categorization, In SIGIR '96: Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 307-315, 1996
  7. Fuhr,N., Hartmanna,S., Lustig,G., Schwantner,M., and Tzeras,K., Air/x - a rule-based multistage indexing systems for large subject fields, In 606-623, editor, Proceedings of RIAO'91, 1991
  8. Gore, K., Cogito Auto Sum: What less can we say? http://slate.msn.com/features/cogitoautosum/cogi toautosum.asp, 2001
  9. Joachims,Thorsten, Text Categorization with Support Vector Machines: Learning with Many Relevant Features, In European Conference on Machine Learning (ECML), Berlin, pages 137-142, 1998. Springer
  10. Koller,D. and Sahami,M., Hierarchically classifying documents using very few words, In The Fourteenth International Conference on Machine Learning (ICML'97), pages 170-178, 1997
  11. Lam,W. and Ho,C.Y., Using a generalized instance set for automatic text categorization, In Proceedings of the 21th Ann. Int. ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98), pages 81-89, 1998
  12. Lewis,D.D. and Ringuette,M., Comparison of two learning algorithms for text categorization, In Proceedings of the Third Annual Symposium on Document Analysis and Information Retrieval (SDAIR'94), 1994
  13. Lie, D.H., Sumatra, A system for Automatic Summary Generation, Carp Technologies, Hengelosestraat 174, 7521 AK Enschede, 2001. http://www.carptechnologies.nl/SumatraTWLT14paper/Sumatra TWLT14.html [14] Masand,B., Linoff, G., and Waltz,D., Classifying news stori