Abstract generation techniques for text
Automatic generation of summaries

Most current RBM methods produce abstracts because they are easy to construct and adapt. The development of abstract generation methods is hampered by the complexity of problems in automatic speech understanding or natural language generation.

In the following, we begin by explaining the working principle of extract-based summary generation methods. Next, we present a few existing automatic extract generation tools, which either have the form of finished software and can be used directly, or are software libraries that can be linked to applications. We then analyze the limitations of extraction-based RBM methods, which leads us to a state-of-the-art review of abstraction-based RBM techniques.