  Recent advancements in transformer-based models have greatly improved the
ability of Question Answering (QA) systems to provide correct answers; in
particular, answer sentence selection (AS2) models, core components of
retrieval-based systems, have achieved impressive results. While generally
effective, these models fail to provide a satisfying answer when all retrieved
candidates are of poor quality, even if they contain correct information. In
AS2, models are trained to select the best answer sentence among a set of
candidates retrieved for a given question. In this work, we propose to generate
answers from a set of AS2 top candidates. Rather than selecting the best
candidate, we train a sequence to sequence transformer model to generate an
answer from a candidate set. Our tests on three English AS2 datasets show
improvement up to 32 absolute points in accuracy over the state of the art.
