  In recent advancements in spoken question answering (QA), end-to-end models
have made significant strides. However, previous research has primarily focused
on extractive span selection. While this extractive-based approach is effective
when answers are present directly within the input, it falls short in
addressing abstractive questions, where answers are not directly extracted but
inferred from the given information. To bridge this gap, we introduce the first
end-to-end Generative Spoken Question Answering (GSQA) model that empowers the
system to engage in abstractive reasoning. The challenge in training our GSQA
model lies in the absence of a spoken abstractive QA dataset. We propose using
text models for initialization and leveraging the extractive QA dataset to
transfer knowledge from the text generative model to the spoken generative
model. Experimental results indicate that our model surpasses the previous
extractive model by 3% on extractive QA datasets. Furthermore, the GSQA model
has only been fine-tuned on the spoken extractive QA dataset. Despite not
having seen any spoken abstractive QA data, it can still closely match the
performance of the cascade model. In conclusion, our GSQA model shows the
potential to generalize to a broad spectrum of questions, thus further
expanding the spoken question answering capabilities of abstractive QA. Our
code is available at https://voidful.github.io/GSQA
