Query-focused Abstractive Summarization via Question-answering Model

Text summarization is a task that creates a short version of a document while preserving the main content. In the age of information explosion, how to obtain the content that users care about from a large amount of information becomes par-ticularly significant. Under these circumstances, query-focused abstractive summarization (QFS) becomes more dominant since it is able to focus on user needs while generating fluent, con-cise, succinct paraphrased summaries. However, different from generic summarization that has achieved remarkable results driven by a large scale of parallel data, the QFS is suffering from lacking enough parallel corpus. To address the above issues, in this paper, we migrate the large-scale generic summarization datasets into query-focused datasets while preserving the informative summaries. Based on the synthetic queries and data, we proposed a new model, called SQAS, which is capable of extracting fine-grained factual information with respect to a specific question, and take into account the reasoning information by understanding the source document leveraged by the question-answering model. Receiving the extracted content, the summary generator can not only generate semantically relevant content but also assure fluent and readable sentences thanks to the language generation capability of a pre-trained language model. Experimental results on both generic datasets and query-focused summary datasets demonstrate the effectiveness of our proposed model in terms of automatic ROUGE metrics and investigating real cases.