  Neural network-based approaches have become widespread for abstractive text
summarization. Though previously proposed models for abstractive text
summarization addressed the problem of repetition of the same contents in the
summary, they did not explicitly consider its information structure. One of the
reasons these previous models failed to account for information structure in
the generated summary is that standard datasets include summaries of variable
lengths, resulting in problems in analyzing information flow, specifically, the
manner in which the first sentence is related to the following sentences.
Therefore, we use a dataset containing summaries with only three bullet points,
and propose a neural network-based abstractive summarization model that
considers the information structures of the generated summaries. Our
experimental results show that the information structure of a summary can be
controlled, thus improving the performance of the overall summarization.
