Much progress has been made in text summarization, fueled by neural
architectures using large-scale training corpora. However, 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 summary
generation by introducing a set of sub-aspect functions (i.e. importance,
diversity, position). These sub-aspect functions are regulated by a set of
control codes to decide which sub-aspect to focus on during summary generation.
We demonstrate that extracted summaries with minimal position bias is
comparable with those generated by standard models that take advantage of
position preference. We also show that news summaries generated with a focus on
diversity can be more preferred by human raters. These results suggest that a
more flexible neural summarization framework providing more control options
could be desirable in tailoring to different user preferences, which is useful
since it is often impractical to articulate such preferences for different
applications a priori.

本研究提出了一种基于神经框架的文本摘要方法，引入了多种子方面的函数（包括重要性、多样性和位置等），可以根据不同控制码来决定在生成文本摘要时关注哪个子方面，以减少位置偏向性并提供更多的用户偏好选择。