Making large-scale support vector machine learning practical
Advances in kernel methods
New Methods in Automatic Extracting
Journal of the ACM (JACM)
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Selecting sentences for answering complex questions
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Heuristics based automatic text summarization of unstructured text
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Natural Language Engineering
CDDS: Constraint-driven document summarization models
Expert Systems with Applications: An International Journal
A zipf-like distant supervision approach for multi-document summarization using wikinews articles
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
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In this paper, we present a Support Vector Machine (SVM) based ensemble approach to combat the extractive multi-document summarization problem. Although SVM can have a good generalization ability, it may experience a performance degradation through wrong classifications. We use a committee of several SVMs, i.e. Cross-Validation Committees (CVC), to form an ensemble of classifiers where the strategy is to improve the performance by correcting errors of one classifier using the accurate output of others. The practicality and effectiveness of this technique is demonstrated using the experimental results.