Making Cognitive Summarization Agents Work In A Real-World Domain

The advantage of cognitively motivated automatic summarizing is that human users can better understand what happens. This improves acceptability. The basic empirical finding in human summarizers is that they combine a choice of intellectual strategies. We report here on SummIt-BMT (Summarize It in Bone Marrow Transplantation), a prototype system that applies a subset of human strategies to a real-world task: fast information supply for physicians in clinical bone marrow transplantation. The human strategies are converted to knowledge-based agents and integrated into a system environment inspired by user-centered information seeking research. A domain ontology provides knowledge shared by human users and system players. Users' query formulation is supported through empirically founded scenarios. Incoming retrieval results are first roughly checked by means of text passage retrieval before the agents apply strategies of competent human summarizers. The presumably relevant text clips are presented with links to their home positions in the source documents. SummIt-BMT has reached the state of a prototype running on a Macintosh server (http://summit-bmt.fh-hannover.de/).

[1]  Nicholas J. Belkin,et al.  Helping people find what they don't know , 2000, CACM.

[2]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part I. Background and Theory , 1997, J. Documentation.

[3]  Brigitte Endres-Niggemeyer,et al.  Summarizing information , 1998 .

[4]  Elisabeth Neugebauer,et al.  Professional summarizing: no cognitive simulation without observation , 1998 .

[5]  John McCarthy,et al.  Notes on Formalizing Context , 1993, IJCAI.

[6]  M. Sanderson.  Advances in Automatic Text Summarization edited by Inderjeet Mani and Mark T. Maybury , 2000 .

[7]  Richard Evans,et al.  A New, Fully Automatic Version of Mitkov's Knowledge-Poor Pronoun Resolution Method , 2002, CICLing.

[8]  David Bawden,et al.  Thesaurus Construction and Use: A Practical Manual , 2000 .

[9]  Gary Marchionini,et al.  Information Seeking in Electronic Environments , 1995 .

[10]  William C. Mann,et al.  RHETORICAL STRUCTURE THEORY: A THEORY OF TEXT ORGANIZATION , 1987 .

[11]  Brigitte Endres-Niggemeyer,et al.  Scenario Forms for Web Information Seeking and Summarizing in Bone Marrow Transplantation , 2002, COLING 2002.

[12]  A. Strauss,et al.  The discovery of grounded theory: strategies for qualitative research aldine de gruyter , 1968 .

[13]  Eduard Hovy,et al.  Automated Text Summarization in SUMMARIST , 1997, ACL 1997.

[14]  E. Guba,et al.  Lincoln, Yvonna, and Egon Guba, "Postpositivism and the Naturalist Paradigm," pp. 14-46 in Yvonna Lincoln and Egon Guba, Naturalistic Inquiry . Beverly Hills, CA: Sage, 1985.* , 1985 .

[15]  Marcia J. Bates,et al.  The design of browsing and berrypicking techniques for the online search interface , 1989 .

[16]  Nicholas J. Belkin,et al.  Ask for Information Retrieval: Part II. Results of a Design Study , 1982, J. Documentation.

[17]  Brigitte Endres-Niggemeyer,et al.  Modelling summary writing by introspection: A small-scale demonstrative study , 1991 .

[18]  W. Kintsch,et al.  Strategies of discourse comprehension , 1983 .