Before revision:  However, reference summaries tend to be position-biased and constructed in an under-constrained fashion, especially for benchmark datasets in the news domain . We propose a neural framework that can flexibly control which sub-aspect functions (i.e. importance, diversity, position)  to focus on during summary generation.
After revision:  However, reference summaries tend to be position-biased and constructed in an under-constrained fashion, especially for benchmark datasets in the news domain , neural models easily overfit by leveraging position-related features due to the prevalence of the inverted pyramid writing style. In addition, there is an unmet need to generate a variety of summaries for different users. In this paper, we propose a neural framework that can flexibly control which sub-aspect functions (i.e. importance, diversity, position) to focus on during summary generation.