  In neural abstractive summarization field, conventional sequence-to-sequence
based models often suffer from summarizing the wrong aspect of the document
with respect to the main aspect. To tackle this problem, we propose the task of
reader-aware abstractive summary generation, which utilizes the reader comments
to help the model produce better summary about the main aspect. Unlike
traditional abstractive summarization task, reader-aware summarization
confronts two main challenges: (1) Comments are informal and noisy; (2) jointly
modeling the news document and the reader comments is challenging. To tackle
the above challenges, we design an adversarial learning model named
reader-aware summary generator (RASG), which consists of four components: (1) a
sequence-to-sequence based summary generator; (2) a reader attention module
capturing the reader focused aspects; (3) a supervisor modeling the semantic
gap between the generated summary and reader focused aspects; (4) a goal
tracker producing the goal for each generation step. The supervisor and the
goal tacker are used to guide the training of our framework in an adversarial
manner. Extensive experiments are conducted on our large-scale real-world text
summarization dataset, and the results show that RASG achieves the
state-of-the-art performance in terms of both automatic metrics and human
evaluations. The experimental results also demonstrate the effectiveness of
each module in our framework. We release our large-scale dataset for further
research.
