附註:Includes bibliographical references and index.
Preface -- Introduction ; 1. Automatic summarizing: Factors and directions -- Section 1: The classical approaches ; 2. The automatic creation of literature abstracts ; 3. New methods in automatic extracting ; 4. Automatic abstracting research at chemical abstracts service -- Section 2: Corpus-based approaches ; 5. A trainable document summarizer ; 6. Development and evaluation of a statistically based document summarization system ; 7. A trainable summarizer with knowledge acquired from robust NLP techniques ; 8. Automated text summarization in SUMMARIST E. -- Section 3: Exploiting discourse structure ; 9. Salience-based content characterization of text documents ; 10. Using lexical chains for text summarization ; 11. Discourse, trees are good indicators of importance in text ; 12. A robust practical text summarizer ; 13. Argumentative classification of extracted sentences as a first step towards flexible abstracting -- Section 4: Knowledge-rich approaches ; 14. Plot units: A narrative summarization strategy ; 15. Knowledge-based text summarization: Salience and generalization operators for knowledge base abstraction ; 16. Generating concise natural language summaries ; 17. Generating summaries from event data -- Section 5: Evaluation methods ; 18. The formation of abstracts by the selection of sentences ; 19. Automatic condensation of electronic publications by sentence selection ; 20. The effects and limitations of automated text condensing on reading comprehension performance ; 21. An evaluation of automatic text summarization systems -- Section 6: New summarization problem areas ; 22. Automatic text structuring and summarization ; 23. Summarizing similarities and differences among related documents ; 24. Generating summaries of multiple news articles ; 25. An empirical study of the optimal presentation of multimedia summaries of broadcast news ; 26. Summarization of diagrams in documents -- Index.
