Multi-Document Summarization using Distributed Bag-of-Words Model
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As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time. In this paper, we present an unsupervised centroid-based document-level reconstruction framework using distributed bag of words model. Specifically, our approach selects summary sentences in order to minimize the reconstruction error between the summary and the documents. We apply sentence selection and beam search, to further improve the performance of our model. Experimental results show that performance of our model is competitive against the state-of-the-art unsupervised algorithms on standard benchmark datasets.
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