Question‐driven text summarization using an extractive‐abstractive framework

Question‐driven automatic text summarization is a popular technique to produce concise and informative answers to specific questions using a document collection. Both query‐based and question‐driven summarization may not produce reliable summaries nor contain relevant information if they do not take advantage of extractive and ive summarization mechanisms to improve performance. In this article, we propose a novel extractive and ive hybrid framework designed for question‐driven automatic text summarization. The framework consists of complimentary modules that work together to generate an effective summary: (1) discovering appropriate non‐redundant sentences as plausible answers using an open‐domain multi‐hop question answering system based on a convolutional neural network, multi‐head attention mechanism and reasoning process; and (2) a novel paraphrasing generative adversarial network model based on transformers rewrites the extracted sentences in an ive setup. Experiments show this framework results in more reliable ive summary than competing methods. We have performed extensive experiments on public datasets, and the results show our model can outperform many question‐driven and query‐based baseline methods (an R1, R2, RL increase of 6%–7% for over the next highest baseline).