  Pre-trained Generative models such as BART, T5, etc. have gained prominence
as a preferred method for text generation in various natural language
processing tasks, including abstractive long-form question answering (QA) and
summarization. However, the potential of generative models in extractive QA
tasks, where discriminative models are commonly employed, remains largely
unexplored. Discriminative models often encounter challenges associated with
label sparsity, particularly when only a small portion of the context contains
the answer. The challenge is more pronounced for multi-span answers. In this
work, we introduce a novel approach that uses the power of pre-trained
generative models to address extractive QA tasks by generating indexes
corresponding to context tokens or sentences that form part of the answer.
Through comprehensive evaluations on multiple extractive QA datasets, including
MultiSpanQA, BioASQ, MASHQA, and WikiQA, we demonstrate the superior
performance of our proposed approach compared to existing state-of-the-art
models.
