Researchers produce thousands of scholarly documents containing valuable
technical knowledge. The community faces the laborious task of reading these
documents to identify, extract, and synthesize information. To automate
information gathering, document-level question answering (QA) offers a flexible
framework where human-posed questions can be adapted to extract diverse
knowledge. Finetuning QA systems requires access to labeled data (tuples of
context, question and answer). However, data curation for document QA is
uniquely challenging because the context (i.e. answer evidence passage) needs
to be retrieved from potentially long, ill-formatted documents. Existing QA
datasets sidestep this challenge by providing short, well-defined contexts that
are unrealistic in real-world applications. We present a three-stage document
QA approach: (1) text extraction from PDF; (2) evidence retrieval from
extracted texts to form well-posed contexts; (3) QA to extract knowledge from
contexts to return high-quality answers -- extractive, abstractive, or Boolean.
Using QASPER for evaluation, our detect-retrieve-comprehend (DRC) system
achieves a +7.19 improvement in Answer-F1 over existing baselines while
delivering superior context selection. Our results demonstrate that DRC holds
tremendous promise as a flexible framework for practical scientific document
QA.

本文提出了一种三阶段文件 QA 方法，包括从 PDF 中提取文本，从提取的文本中检索证据以形成良好的上下文，以及从上下文中提取 3 种类型的知识（抽取式、生成式或布尔式）。通过对 QASPER 的评估，DRC 系统在交付更优的上下文选择的同时，Answer-F1 达到了 +7.19 的提高。结果表明，DRC 在科学文献 QA 中有很大的亮点。