The Rule of Three: Abstractive Text Summarization in Three Bullet Points
This work addresses the issue of variable-length summaries in abstractive text summarization for NLP researchers, but it is incremental as it builds on existing neural network approaches.
The paper tackled the problem of abstractive text summarization by focusing on controlling the information structure of summaries, using a dataset with three-bullet-point summaries to improve performance.
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.