Sharif Digital Repository

In recent years, deep neural networks have achieved significant improvements in the field of automatic text summarization by using neural sequence architectures. However,the results of these improvements are more tangible in the production of short summaries (a few words or single sentences). In the field of producing long (multisentence) abstracts, the presented models suffer from several issues; These models produce the details of the events incorrectly and tend to generate the phrases been produced before repeatedly. The wording from the output of these models is very close to the original text. Also, the metrics used to evaluate the quality of produced summaries do not have the ability...