MemSum-DQA: Adapting An Efficient Long Document Extractive Summarizer
  for Document Question Answering
Nianlong Gu Yingqiang Gao Richard H. R. Hahnloser
We introduce MemSum-DQA, an efficient system for document question answering
(DQA) that leverages MemSum, a long document extractive summarizer. By
prefixing each text block in the parsed document with the provided question and
question type, MemSum-DQA selectively extracts text blocks as answers from
documents. On full-document answering tasks, this approach yields a 9%
improvement in exact match accuracy over prior state-of-the-art baselines.
Notably, MemSum-DQA excels in addressing questions related to
child-relationship understanding, underscoring the potential of extractive
summarization techniques for DQA tasks.