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

Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering