  Query Focused Summarization (QFS) has been addressed mostly using extractive
methods. Such methods, however, produce text which suffers from low coherence.
We investigate how abstractive methods can be applied to QFS, to overcome such
limitations. Recent developments in neural-attention based sequence-to-sequence
models have led to state-of-the-art results on the task of abstractive generic
single document summarization. Such models are trained in an end to end method
on large amounts of training data. We address three aspects to make abstractive
summarization applicable to QFS: (a)since there is no training data, we
incorporate query relevance into a pre-trained abstractive model; (b) since
existing abstractive models are trained in a single-document setting, we design
an iterated method to embed abstractive models within the multi-document
requirement of QFS; (c) the abstractive models we adapt are trained to generate
text of specific length (about 100 words), while we aim at generating output of
a different size (about 250 words); we design a way to adapt the target size of
the generated summaries to a given size ratio. We compare our method (Relevance
Sensitive Attention for QFS) to extractive baselines and with various ways to
combine abstractive models on the DUC QFS datasets and demonstrate solid
improvements on ROUGE performance.
