Comparative Evaluation of Term-Weighting Methods for Automatic Summarization*

References

• Baxendale , P. B. 1958 . Man-made index for technical literature – an experiment . IBM Journal of Research and Development , 2 ( 4 ) : 354 – 361 .
   Web of Science ®Google Scholar
• Black , W. J. and Johnson , F. C. 1988 . A practical evaluation of two rule-based automatic abstracting techniques . Expert Systems for Information Management , 1 ( 3 ) : 159 – 177 .
   Google Scholar
• Brandow , R. , Mitze , K. and Rau , L. F. 1995 . Automatic condensation of electronic publications by sentence selection . Information Processing and Management , 31 ( 5 ) : 675 – 685 .
   Google Scholar
• Buckley , C. 1985 . “ Implementation of the SMART information retrieval system ” . In Technical Report 85–686 , Cornell University .
   Google Scholar
• Burnard , L. 1992 . “ The text encoding initiative: A progress report ” . In Topics in English Linguistics , Edited by: Traugott , E. and Kortmann , B. 97 – 107 . Berlin : Mouton de Gruyter .
   Google Scholar
• Church , K. One term or two? . Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . Seattle, Washington, United States. pp. 310 – 318 .
   Google Scholar
• Donaway , R. L. , Drummey , K. W. and Mather , L. A. A comparison of rankings produced by summarization evaluation measures . Proceedings of NAACL-ANLP 2000 Workshop on Text Summarization . Seattle, Washington. pp. 69 – 78 .
   Google Scholar
• Edmundson , H. P. 1969 . New methods in automatic extracting . Journal of the Association for Computing Machinery , 16 ( 2 ) : 264 – 285 .
   Web of Science ®Google Scholar
• Frakes W. B. Baeza-Yates R. Information Retrieval: Data Structures and Algorithms Prentice Hall Englewood Cliffs, NJ 1992
   Google Scholar
• Ha , L. A. and Orăsan , C. Concept-centred summarization: Producing glossary entries for terms using summarization methods . Proceedings of RANLP2005 . Borovets, Bulgaria. pp. 219 – 225 .
   Google Scholar
• Harman , D. 1991 . How effective is suffixing? . Journal of American Society for Information Sciences , 42 ( 1 ) : 7 – 15 .
   Google Scholar
• Hovy , E. 2003 . “ Text summarization ” . In The Oxford Handbook of Computational Linguistics , Edited by: Mitkov , R. 583 – 598 . Oxford : Oxford University Press .
   Google Scholar
• Hull , D. A. 1996 . Stemming algorithms: a case study for detailed evaluation . Journal of American Society for Information Sciences , 47 ( 1 ) : 70 – 84 .
   Web of Science ®Google Scholar
• Krovetz , R. Viewing morphology as an inference process . Proceedings of the Sixteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . Pittsburgh, Pennsylvania, United States. pp. 191 – 202 .
   Google Scholar
• Kupiec , J. , Pederson , J. and Chen , F. A trainable document summarizer . Proceedings of the 18th ACM/SIGIR Annual Conference on Research and Development in Information Retrieval . Seattle. pp. 68 – 73 .
   Google Scholar
• Lin , C.-Y. Assembly of topic extraction modules in SUMMARIST . AAAI Spring Symposium on Intelligent Text Summarization . Stanford, California, USA. pp. 44 – 50 .
   Google Scholar
• Lin , C.-Y. and Hovy , E. Identifying topic by position . Proceedings of the 5th Conference on Applied Natural Language Processing . Washington, DC. pp. 283 – 290 .
   Google Scholar
• Luhn , H. P. 1958 . The automatic creation of literature abstracts . IBM Journal of Research and Development , 2 ( 2 ) : 159 – 165 .
   Web of Science ®Google Scholar
• Lyman , P. and Varian , H. R. 2003 . “ How much information – 2003 ” . In Technical report , School of Information Management and Systems, University of California at Berkeley .
   Google Scholar
• Mani , I. 2001 . Automatic Summarization , Amsterdam : John Benjamins Publishing Company .
   Google Scholar
• Manning , C. D. and Schütze , H. 1999 . Foundations of Statistical Natural Language Processing , Cambridge, MA : The MIT Press .
   Google Scholar
• Mladenic , D. and Grobelnik , M. Feature selection for classification based on text hierarchy . Proceedings of Conference on Automated Learning and Discovery CONALD-98 . Pittsburgh, PA. pp. 1 – 6 .
   Google Scholar
• Nanas , N. , Uren , V. and De Roeck , A. 2003 . “ A comparative study of term weighting methods for information filtering ” . In Technical Report kmi-03-4 , KMi, Open University .
   Google Scholar
• Noreault , T. , McGill , M. and Koll , M. B. A performance evaluation of similarity measures, document term-weighting schemes and representations in a boolean environment . Proceedings of the 3rd Annual ACM Conference on Research and Development in Information Retrieval (SIGIR'80) . Kent, UK. pp. 57 – 76 .
   Google Scholar
• Orăsan , C. , Pekar , V. and Hasler , L. A comparison of summarization methods based on term specificity estimation . Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC2004) . Lisbon, Portugal. pp. 1037 – 1041 .
   Google Scholar
• Porter , M. F. 1980 . An algorithm for suffix stripping . Program , 14 ( 3 ) : 130 – 137 .
   Google Scholar
• Riloff , E. Little words can make a big difference for text classification . Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval . Seattle, Washington, United States. pp. 130 – 136 .
   Google Scholar
• Robertson , S. and Sparck Jones , K. 1976 . Relevance weighting of search terms . Journal of the American Society for Information Science , 27 ( 3 ) : 129 – 146 .
   Web of Science ®Google Scholar
• Rose , T. G. , Stevenson , M. and Whitehead , M. The Reuters Corpus Volume 1 – from Yesterday's News to Tomorrow's Language Resources . Proceedings of the Third International Conference on Language Resources and Evaluation (LREC-2002) . Las Palmas de Gran Canaria. pp. 827 – 833 .
   Google Scholar
• Salton , G. and Buckley , C. 1988 . Term-weighting approaches in automatic text retrieval . Information Processing and Management , 24 ( 5 ) : 513 – 523 .
   Web of Science ®Google Scholar
• Salton , G. and McGill , M. J. 1983 . Introduction to Modern Information Retrieval , New York : McGraw-Hill .
   Google Scholar
• Sparck Jones , K. 1972 . A statistical interpretation of term specificity and its application in term retrieval . Journal of Documentation , 28 ( 1 ) : 11 – 21 .
   Web of Science ®Google Scholar
• Tapanainen , P. and Järvinen , T. A non-projective dependency parser . Proceedings of the 5th Conference of Applied Natural Language Processing . Washington DC, USA. pp. 64 – 71 .
   Google Scholar
• Teufel , S. 1999 . Argumentative Zoning: Information Extraction from Scientific Text Unplublished PhD thesis, University of Edinburgh
   Google Scholar
• Teufel , S. and Moens , M. Sentence extraction as a classification task . Proceedings of the ACL'97/EACL'97 Workshop on Intelligent Scalable Text Summarization . Madrid, Spain. pp. 58 – 59 .
   Google Scholar
• Teufel , S. and Moens , M. 2002 . Summarizing scientific articles: Experiments with relevance and rhetorical status . Computational Linguistics , 28 ( 4 ) : 409 – 445 .
   Google Scholar
• Yamamoto , M. and Church , K. W. 2001 . Using suffix arrays to compute term frequency and document frequency for all substrings in a corpus . Computional Linguistics , 27 ( 1 ) : 1 – 30 .
   Google Scholar
• Zechner , K. Fast generation of abstracts from general domain text corpora by extracting relevant sentences . COLING – 96, The International Conference on Computational Linguistics . Copenhagen, Denmark. pp. 986 – 989 .
   Google Scholar