Summary sentence classification is an important step to generate document surrogates known as summary extracts. The quality of an extract depends much on the correctness of this step. We aim to classify potential summary sentences using a statistical learning method that models sentences according to a linguistic technique which examines writing styles, known as Stylometry. The sentences in documents are represented using a novel set of stylometric attributes. For learning, an innovative two-stage classification is set up that comprises two learners in subsequent steps: k-Nearest Neighbour and Naive Bayes. We train and test the learners with the newswire documents collected from two benchmark datasets, viz., the CAST and the DUC2002 datasets. Extensive experimentation strongly suggests that our method has outstanding performance for the single document summarization task. However, its performance is mixed for classifying summary sentences from multiple documents. Finally, comparisons show that our method performs significantly better than most of the popular extractive summarization methods.
['Rushdi Shams', 'Robert E. Mercer']
Summary Sentence Classification Using Stylometry