Экстрактивный суммаризатор

This paper describes the design and evaluation of an extractive summarizer for educational science content called COGENT. COGENT extends MEAD based on strategies elicited from an em- pirical study with …...

We introduce a supervised model for predicting word importance that incorporates a rich set of features. Our model is superior to prior approaches for identifying words used in human summaries. …...

AbstractAccessing medical literature is difficult for laypeople as the content is written for specialists and contains medical jargon. Automated text simplification methods offer a potential means to address this issue. …...