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Neural network-based approaches have become widespread for abstractive textsummarization. Though previously proposed models for abstractive textsummarization addressed the problem of repetition of the same contents in thesummary, they did not explicitly consider its information structure. One of thereasons these previous models failed to account for information structure inthe generated summary is that standard datasets include summaries of variablelengths, resulting in problems in analyzing information flow, specifically, themanner 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 thatconsiders the information structures of the generated summaries. Ourexperimental results show that the information structure of a summary can becontrolled, thus improving the performance of the overall summarization.

The Rule of Three: Abstractive Text Summarization in Three Bullet Points