  Query focused summarization (QFS) models aim to generate summaries from
source documents that can answer the given query. Most previous work on QFS
only considers the query relevance criterion when producing the summary.
However, studying the effect of answer relevance in the summary generating
process is also important. In this paper, we propose QFS-BART, a model that
incorporates the explicit answer relevance of the source documents given the
query via a question answering model, to generate coherent and answer-related
summaries. Furthermore, our model can take advantage of large pre-trained
models which improve the summarization performance significantly. Empirical
results on the Debatepedia dataset show that the proposed model achieves the
new state-of-the-art performance.
