C4.5: programs for machine learning
C4.5: programs for machine learning
A trainable document summarizer
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic text structuring and summarization
Information Processing and Management: an International Journal - Special issue: methods and tools for the automatic construction of hypertext
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Why Discretization Works for Naive Bayesian Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Generating Text Summaries through the Relative Importance of Topics
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
Automatic Text Summarization Using a Machine Learning Approach
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
TextTiling: A Quantitative Approach to Discourse
TextTiling: A Quantitative Approach to Discourse
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The automatic creation of literature abstracts
IBM Journal of Research and Development
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Combining Multiple Features for Automatic Text Summarization through Machine Learning
PROPOR '08 Proceedings of the 8th international conference on Computational Processing of the Portuguese Language
A complex network approach to text summarization
Information Sciences: an International Journal
Automatic summarization for text simplification: evaluating text understanding by poor readers
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
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This paper presents a novel approach to combining features for training an automatic extractive summarizer of texts written in Brazilian Portuguese. The approach aims at both diminishing the effort of classifying features that are representative for Automatic Summarization and providing more informativeness for the summarizer to decide which text spans to include in an extract. Finding a balanced set of features is explored through WEKA. We discuss several ways of modifying the feature set and show how automatic feature selection may be useful for customizing the summarizer.