Multilabel Associative Text Classification Using Summarization (International Journal of Engineering and Computer Science, 2014)
This paper deals with the concern of curse of dimensionality in the Text Classification  problem using Text Summarization. Classification and association rule mining can produce well-organized as well as precise classifiers than established techniques [1]. However, associative classification technique still suffers from the vast set of mined rules. Thus, this work brings in advantages of Automatic Text Summarization. Since text summarization is based on identifying the set of sentences that are most important for the overall understanding of document(s). These techniques use the  dataset to mine rules and then filter and/or rank the discovered rules to help the user in identifying useful ones. Finally, for experimentation, the Reuter-21578 dataset are used and thus the obtained outputs have ensured that the performance of the approach has been effectively improved with regards to classification accuracy, number of derived rules and training time.