C4.5: programs for machine learning
C4.5: programs for machine learning
An algorithm for pronominal anaphora resolution
Computational Linguistics
Summarizing scientific articles: experiments with relevance and rhetorical status
Computational Linguistics - Summarization
Automatic summarization of open-domain multiparty dialogues in diverse genres
Computational Linguistics - Summarization
Linguistic indicators for language understanding: using machine learning methods to combine corpus-based indicators for aspectual classification of clauses
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
An empirically based system for processing definite descriptions
Computational Linguistics
A non-projective dependency parser
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A multilingual paradigm for automatic verb classification
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Customization in a unified framework for summarizing medical literature
Artificial Intelligence in Medicine
A Gradual Combination of Features for Building Automatic Summarisation Systems
TSD '09 Proceedings of the 12th International Conference on Text, Speech and Dialogue
Text summarisation in progress: a literature review
Artificial Intelligence Review
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This paper describes a system that produces extractive summaries of short works of literary fiction. The ultimate purpose of produced summaries is defined as helping a reader to determine whether she would be interested in reading a particular story. To this end, the summary aims to provide a reader with an idea about the settings of a story (such as characters, time and place) without revealing the plot. The approach presented here relies heavily on the notion of aspect. Preliminary results show an improvement over two naïve baselines: a lead baseline and a more sophisticated variant of it. Although modest, the results suggest that using aspectual information may be of help when summarizing fiction. A more thorough evaluation involving human judges is under way.