Automatic summarising: factors and directions

This position paper suggests that progress with automatic summarising demands a better research methodology and a carefully focussed research strategy. In order to develop effective procedures it is necessary to identify and respond to the context factors, i.e. input, purpose, and output factors, that bear on summarising and its evaluation. The paper analyses and illustrates these factors and their implications for evaluation. It then argues that this analysis, together with the state of the art and the intrinsic difficulty of summarising, imply a nearer-term strategy c on shallow, but not surface, text analysis and on indicative summarising. This is illustrated with current work, from which a potentially productive research programme can be developed.

[1]  Gerard Salton,et al.  Automatic Text Structuring and Summarization , 1997, Inf. Process. Manag..

[2]  Kathleen McKeown,et al.  Generating Concise Natural Language Summaries , 1995, Inf. Process. Manag..

[3]  Karen Sparck Jones Discourse modelling for automatic summarising , 1993 .

[4]  Gerard Salton,et al.  A new comparison between conventional indexing (MEDLARS) and automatic text processing (SMART) , 1972, J. Am. Soc. Inf. Sci..

[5]  Jennifer Rowley,et al.  Abstracting and indexing , 1982 .

[6]  Karen Sparck Jones,et al.  Book Reviews: Evaluating Natural Language Processing Systems: An Analysis and Review , 1996, CL.

[7]  Chris D. Paice,et al.  Constructing literature abstracts by computer: Techniques and prospects , 1990, Inf. Process. Manag..

[8]  Daniel Marcu,et al.  From discourse structures to text summaries , 1997 .

[9]  Margaret King,et al.  Evaluating natural language processing systems , 1996, CACM.

[10]  H. Alshawi,et al.  The Core Language Engine , 1994 .

[11]  Sheryl R. Young,et al.  Automatic Classification and Summarization of Banking Telexes , 1985, CAIA.

[12]  Udo Hahn,et al.  Centering in-the-large: computing referential discourse segments , 1997 .

[13]  Inderjeet Mani,et al.  Multi-Document Summarization by Graph Search and Matching , 1997, AAAI/IAAI.

[14]  Branimir K. Boguraev,et al.  Salience-based Content Characterisafion of Text Documents , 1997 .

[15]  Robert J. Gaizauskas,et al.  POETIC: A system for gathering and disseminating traffic information , 1995, Natural Language Engineering.

[16]  E. F. Skorochod'ko Adaptive Method of Automatic Abstracting and Indexing , 1971, IFIP Congress.

[17]  Gerard Salton,et al.  Another look at automatic text-retrieval systems , 1986, CACM.

[18]  Cyril W. Cleverdon A comparative evaluation of searching by controlled language and natural language in experimental N.A.S.A. data base , 1977 .

[19]  Karen Spärck Jones What Might be in a Summary? , 1993, Information Retrieval.

[20]  B. Endres-Niggemeyer Summarising text for intelligent communication. Results of the Dagstuhl seminar , 1994 .

[21]  Marti A. Hearst Multi-Paragraph Segmentation Expository Text , 1994, ACL.

[22]  Julia Galliers,et al.  Evaluating natural language processing systems , 1995 .

[23]  Lisa F. Rau,et al.  Automatic Condensation of Electronic Publications by Sentence Selection , 1995, Inf. Process. Manag..