Multilayer CARU Model for Text Summarization

Sio Kei Im, Ka-Hou Chan

Abstract: With the large amount of data available today, text summaries have become very important to get the right amount of information from a large amount of text. As can be seen, the articles in this review present different approaches to creating long summaries. Various studies have investigated different methods for summarizing text. In most cases, the methods described in this paper produce summaries or excerpt summaries of text documents, and a query-based summarization technique is also described. These techniques are structure and semantic based methods for summarizing text documents. Experimental results on the DUC-2002 dataset show that our system outperforms state of-the-art extractive and abstractive baselines on the ROUGE evaluation metric.