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Computational Approaches

  • Brigitte Endres-Niggemeyer
  • Kai Haseloh
  • Jens Müller
  • Simone Peist
  • Irene Santini de Sigel
  • Alexander Sigel
  • Elisabeth Wansorra
  • Jan Wheeler
  • Brünja Wollny

Abstract

We have so far considered summarization and communication in communication situations which from a technical point of view were very simple. A typical mass communication situation was mentioned, where in contrast to face-to-face communication, the communicators do not have to be in the same place at the same time in order to communicate. However, the television channels, satellite dishes, telephones, and other telecommunications devices, all the technical equipment necessary to make spatiotemporally displaced communication possible, remained in the background. For the time being, we assumed that technical communication media do not affect the communication of contents in mass communication situations. It makes no difference whether the summary of the week’s stock exchange developments is transmitted via satellite or the telephone network. However, it does make a difference whether a summary is produced for television or radio, because the communication medium determines the presentation possibilities, and it does make a difference whether a summary has to comply with the constraints of an organized professional information environment or not.

Keywords

Computational Approach Noun Phrase Sentence Length Defense Advance Research Project Agency Defense Advance Research Project Agency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. (AYUS92).
    Ayuso, D.; Boisen, S.; Fox, H.; Gish, H.; Ingria, R. Weischedel, R. (1992): BBN: Description of the PLUM system as used for MUC-4. MUC-4, pp. 169–176Google Scholar
  2. (BARZ97).
    Barzilay, R.; Elhadad, M. (1997): Using lexical chains for text summarization. MANI97, pp. 10–17Google Scholar
  3. (BOGU97).
    Boguraev, B.; Kennedy, C. (1997): Salience-based content characterization of text documents. MANI97, pp. 2–9Google Scholar
  4. (BORK68).
    Borko, H. ed. (1968): Automated language processing. New York: WileyMATHGoogle Scholar
  5. (BRAN95).
    Brandow, R.; Mitze, K.; Rau, L.F. (1995): Automatic condensation of electronic publications by sentence selection. Information Processing and Management 31:5,675–685CrossRefGoogle Scholar
  6. (CLAU87).
    Claus, H. (1987): Zur Objektivierung der Inhaltserschliessung russischsprachiger Zeitschriftenliteratur des Fachgebietes Informationstechnik im Rahmen der wissenschaftlich-technischen Informationstätigkeit und der fachsprachlichen Fremdsprachenausbildung. [Improving the objectivity of content analysis of Russian information technology journals within the framework of scientific and technical information and of technical translation education]. PhD Thesis, University of LeipzigGoogle Scholar
  7. (CRI92) CRI (1992): SIMPR at the SIGIR 1992 Conference ExhibitionGoogle Scholar
  8. (DANE74).
    Danes, F. (1974): Functional sentence perspective and the organization of text. Danes, F. ed.: Papers in functional sentence perspective, pp. 106–128. Den Haag: MoutonGoogle Scholar
  9. (DEJ082).
    DeJong, G. (1982): An overview of the FRUMP system. LEHN82, pp. 149–175Google Scholar
  10. (EDMU61).
    Edmundson, H. P. (1961): Automatic abstracting and indexing: Survey and recommendations. Comm. ACM 5:5,226–235CrossRefGoogle Scholar
  11. (EDMU63).
    Edmundson, H. P. (1963): Automatic abstracting. TRW Computer Division, Thompson Ramo Wooldridge, Inc., Canoga Park, CAGoogle Scholar
  12. (EDMU69).
    Edmundson, H.P. (1970): New methods in automatic extracting. J. ACM 16:2,264–285CrossRefGoogle Scholar
  13. (FUM82).
    Fum, D.; Guida, G.; Tasso, C. (1982): Forward and backward reasoning in automatic abstracting. COLING-82: Proc. 9th International Conference on Computational Linguistics. Prague, pp. 83–88.Google Scholar
  14. (FUM84).
    Fum, D.; Guida, G.; Tasso, C. (1984): A propositional language for text representation. Bara, B.G.; Guida, G. eds.: Computational models of natural language processing, pp. 121–150. Amsterdam: North-HollandGoogle Scholar
  15. (FUM85a).
    Fum, D.; Guida, G.; Tasso, C. (1985): Evaluating importance: A step towards text summarization. IJCAI-85: Proc. 9th International Joint Conference on Artificial Intelligence, pp. 840–844. Los Altos, CA: KaufmannGoogle Scholar
  16. (FUM85b).
    Fum, D.; Guida, G.; Tasso, C. (1985): A rule-based approach to evaluating importance in descriptive texts. Proc. EACL85. Second conference of the European Chapter of the Association for Computational Linguistics. 25–29 March 1985, Geneva, Switzerland, pp. 244–250Google Scholar
  17. (GIBB90).
    Gibb, F.; Smart, G. (1990): Structured information management using new techniques for processing text. Online Review 14:3, 159–171CrossRefGoogle Scholar
  18. (GIBB93).
    Gibb, F. (1993): Knowledge-based indexing in SIMPR: Integration of natural language processing and principles of subject analysis in an automated indexing system. J. Document and Text Management 1:2,131–153Google Scholar
  19. (HAHN90).
    Hahn, U. (1990): TOPIC parsing: Accounting for text macro structures in full-text analysis. Information Processing and Management 26:1,135–170CrossRefGoogle Scholar
  20. (HAND97).
    Hand, T. F. (1997): A proposal for task-based evaluation of text summarization systems. MANI97, pp. 31–38Google Scholar
  21. (HEAR93).
    Hearst, M.A.; Plaunt, C. (1993): Subtopic structuring for full-length document access. Proc. ACM-SIGIR ’93, pp. 59–68. New York: ACMGoogle Scholar
  22. (HEAR94).
    Hearst, M. (1994): Multi-paragraph segmentation of expository text. Proc. 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, NM, June 27–30, pp. 9–16Google Scholar
  23. (HOVY88).
    Hovy, E. (1988): Generating natural language under pragmatic constraints. Hillsdale, NJ: ErlbaumGoogle Scholar
  24. (HOVY93).
    Hovy, E. (1993): Automated discourse generation using discourse structure relations. Artificial Intelligence 63:341–385CrossRefGoogle Scholar
  25. (HOVY97).
    Hovy, E.; Lin, C.Y. (1997): Automated text summarization in SUMMARIST. MANI97, pp. 18–24Google Scholar
  26. (JACO90).
    Jacobs, P.S.; Rau, L.F. (1990): SCISOR: Extracting information from online news. Comm. ACM 33:11,88–97CrossRefGoogle Scholar
  27. (JACO92).
    Jacobs, P.S. (1992): TRUMP: a transportable language understanding program. Int. J. Intelligent Systems 7:245–276CrossRefGoogle Scholar
  28. (JACO93).
    Jacobs, P.S.; Rau, L.F. (1993): Innovations in text interpretation. Artificial Intelligence 63:143–191CrossRefGoogle Scholar
  29. (JONE93).
    Sparck Jones, K. (1993): What might be in a summary? Knorz, G.; Krause, J.; Womser-Hacker, C., eds. Information Retrieval ’93: Von der Modellierung zur Anwendung [From modeling to application], pp. 9–26. Konstanz: UniversitätsverlagGoogle Scholar
  30. (JOHN93).
    Johnson, F.C.; Paice, C.D.; Black, W.J.; Neal, A.P. (1993): The application of linguistic processing to automatic abstract generation. J. Document and Text Management 1:3,215.23–9Google Scholar
  31. (JUST95).
    Justeson, J.S.; Katz, S.M. (1995): Technical terminology: some linguistic properties and an algorithm for identifcation in text. Natural Language Engineering 1:1,9–27CrossRefGoogle Scholar
  32. (KARE91).
    Karernyk, D.; Karlsson, F.; Smart, G. (1991): Knowledge-based indexing of morphosyntactically analysed language. Expert Systems for Information Management 4:1–29Google Scholar
  33. (KINT74).
    Kintsch, W. (1974): The representation of meaning in memory. Hillsdale, NJ: ErlbaumGoogle Scholar
  34. (KINT78).
    Kintsch, W.; Dijk, T.A. van (1978): Toward a model of text comprehension. Psychological Review 85:363–394CrossRefGoogle Scholar
  35. (KINT83).
    Kintsch, W.; Dijk, T.A. van (1983): Strategies of Discourse Comprehension. Orlando, FL: Academic PressGoogle Scholar
  36. (KRUP92).
    Krupka, G.; Jacobs, P.; Rau, L.(1992): GE NLToolset: Description of the system as used for MUC-4. MUC-4, pp. 177–185Google Scholar
  37. (KUHL89).
    Kuhlen, R.; Hammwoehner, R.; Thiel, U. (1989): TWRM-TOPOGRAPHIC. Informatik Forschung und Entwicklung 4:89–107.Google Scholar
  38. (KUPI95).
    Kupiec, J.; Pedersen, J.; Chen, F. (1995): A trainable document summarizer. SIGI95, pp. 68–73Google Scholar
  39. (LEHN82).
    Lehnert, W.G.; Ringle, M.H. eds. (1982): Strategies for natural languages processing. Hillsdale, NJ: ErlbaumGoogle Scholar
  40. (LAPP94).
    Lappin, S.; Leass, H. (1994): An algorithm for pronominal anaphora resolution. Computational Linguistics, 20:4,535–561Google Scholar
  41. (LUHN58).
    Luhn, H.P. (1958): The automatic creation of literature abstracts. IBM J. Research and Development 2:2,159–165CrossRefMathSciNetGoogle Scholar
  42. (MANI97).
    Mani, L; Maybury, M. eds. (1997): Intelligent scalable text summarization. Proc. of a workshop sponsored by the Association for Computational Linguistics. Madrid, July 1997Google Scholar
  43. (MANN88).
    Mann, W.C.; Thompson, S.A. (1988): Rhetorical Structure Theory: Toward a functional theory of text organization. Text 8:3,243–281CrossRefGoogle Scholar
  44. (MARC97a).
    Marcu, D. (1997): The rhetorical parsing of natural language texts. Proc. 35th Annual Meeting of the Association for Computational Linguistics, Madrid, July 1997, pp. 96–103. San Francisco, CA: Morgan KaufmannGoogle Scholar
  45. (MARC97b).
    Marcu, D. (1997): From discourse structures to text summaries. MANI97, pp. 82–88Google Scholar
  46. (MATH72).
    Mathis, B.A. (1972): Techniques for the evaluation and improvement of computer-produced abstracts. Columbus OH: Ohio State Univ. Technical Report OSU-CISRC-TR-72–15MATHGoogle Scholar
  47. (MAYB95).
    Maybury, M.T. (1995): Generating summaries from event data. Information Processing and Management 31:5,735–751CrossRefGoogle Scholar
  48. (MCKE95a).
    McKeown, K.; Robin, J.; Kukich, K. (1995): Generating concise natural language summaries. Information Processing and Management 31:5,703–733CrossRefGoogle Scholar
  49. (MCKE95b).
    McKeown, K.; Radev, D.R. (1995): Generating summaries of multiple news articles. SIGI95, pp. 74–82Google Scholar
  50. (MITR97).
    Mitra, M.; Singhal, A.; Buckley, C. (1997): Automatic text summarization by paragraph extraction. MANI97, pp. 39–46Google Scholar
  51. (MUC-3).
    Defense Advanced Research Projects Agency (DARPA): Proc. Third Message Understanding Conference, May 1991. San Mateo, CA: Morgan KaufmannGoogle Scholar
  52. (MUC-4).
    Defense Advanced Research Projects Agency (DARPA): Proc. Fourth Message Understanding Conference, June 1992. San Mateo, CA: Morgan KaufmannGoogle Scholar
  53. (ONO94).
    Ono, K.; Sumita, K.; Miike, S. (1994): Abstract generation based on rhetorical structure extraction. COLING 94. Proc. 15th International Conference on Computational Linguistics. Kyoto, August 1994, pp. 344–348Google Scholar
  54. (PAIC81).
    Paice, C.D. (1981): The automatic generation of literature abstracts. Oddy, R.N.; Rijsbergen, C.J.; Williams, P.W. eds.: Information Retrieval Research, pp. 172–191. London: ButterworthsGoogle Scholar
  55. (PAIC90).
    Paice, C.D. (1990): Constructing literature abstracts by computer: Techniques and prospects. Information Processing and Management 26:1,171–186CrossRefGoogle Scholar
  56. (RATH61).
    Rath, G.J.; Resnick, A.; Savage, T.R. (1961): The formation of abstracts by the selection of sentences. Part 1. Sentence selection by men and machines. American Documentation 12:2,139–141CrossRefGoogle Scholar
  57. (RAU87).
    Rau, L.F. (1987): Knowledge organization and access in a conceptual information system. Information Processing and Management 23:4,269–283Google Scholar
  58. (RAU89).
    Rau, L.F.; Jacobs, P.S.; Zernik, U. (1989): Information extraction and text summarization using linguistic knowledge acquisition. Information Processing and Management 25:4,419–428CrossRefGoogle Scholar
  59. (RESN61).
    Resnick, A. (1961): The formation of abstracts by the selection of sentences. Part 2. The reliability of people in selecting sentences. American Documentation 12:2,141–143CrossRefMathSciNetGoogle Scholar
  60. (RUSH71).
    Rush, J.E.; Salvador, R.; Zamora, A. (1971): Automatic abstracting and indexing. II. Production of indicative abstracts by application of contextual inference and syntactic coherence criteria. J. American Society for Information Science 22:4,260–274CrossRefGoogle Scholar
  61. (SALT71).
    Salton, G., ed. (1971): The SMART Retrieval System. Experiments in Automatic Document Processing. Englewood Cliffs, NJ: Prentice HallGoogle Scholar
  62. (SALT89).
    Salton, G.(1989): Automatic text processing. The transformation, analysis and retrieval of information by computer. Reading, MA: Addison-WesleyGoogle Scholar
  63. (SALT97).
    Salton, G.; Singhal, A.; Mitra, M.; Buckley, C. (1997): Automatic Text Structuring and Summarization, Information Processing and Management 33:2,193–208CrossRefGoogle Scholar
  64. (SIGI95).
    Fox, E. A.; Ingwersen, P.; Fidel, R. eds. (1995): Proc. 18th ACM-SIGIR Conference on Research and Development in Information Retrieval. Seattle, July 1995Google Scholar
  65. (SKOR71).
    Skorohodko, E.F. (1971): Adaptive method of automatic abstracting and indexing. Proc. IFIP Conference 1971, Lubljana, booklet TA-6, pp. 133–137Google Scholar
  66. (SKOR81).
    Skorohodko, E.F. (1981): Semantische Relationen in der Lexik und in Texten [Semantic relations in the lexicon and in texts]. Bochum: BrockmeyerGoogle Scholar
  67. (SMAL82).
    Small, S.; Rieger, C. (1982): Parsing and comprehension with word experts. LEHN82, pp. 89–147Google Scholar
  68. (SMAR93).
    Smart, G. (1993): Using language analysis to manage information. Aslib Proc. 45:5,123–129CrossRefGoogle Scholar
  69. (TEUF97).
    Teufel, S.; Moens, M. (1997): Sentence extraction as a classification task. MANI97, pp. 58–65Google Scholar
  70. (TRAB85a).
    Trabasso T.; Sperry, L. (1985): Causal relatedness and importance of story events. J. Memory and Language 24:595–611CrossRefGoogle Scholar
  71. (TRAB85b).
    Trabasso, T.; Broek, P. van den (1985): Causal thinking and the representation of narrative events. J. Memory and Language 24:612–630CrossRefGoogle Scholar
  72. (WYLL68).
    Wyllys, R.E. (1968): Extracting and abstracting by computer. BORK68, pp. 127–179Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Brigitte Endres-Niggemeyer
    • 1
  • Kai Haseloh
  • Jens Müller
  • Simone Peist
  • Irene Santini de Sigel
  • Alexander Sigel
  • Elisabeth Wansorra
  • Jan Wheeler
  • Brünja Wollny
  1. 1.Fachhochschule Hannover, Information and Communication DepartmentUniversity of Applied SciencesHannoverGermany

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