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.

翻译：我们提出MemSum-DQA，一种基于长文档抽取式摘要模型MemSum的高效文档问答系统。通过将解析后文档中的每个文本块与给定问题及问题类型进行前缀拼接，MemSum-DQA能够从文档中抽取文本块作为答案。在全文档问答任务中，该方法在精确匹配准确率上相较于先前最先进基线模型提升了9%。值得注意的是，MemSum-DQA在处理与子关系理解相关的问题时表现尤为突出，这彰显了抽取式摘要技术用于文档问答任务的潜力。