Recent efforts in bioinformatics have achieved tremendous progress in the
machine reading of biomedical literature, and the assembly of the extracted
biochemical interactions into large-scale models such as protein signaling
pathways. However, batch machine reading of literature at today's scale (PubMed
alone indexes over 1 million papers per year) is unfeasible due to both cost
and processing overhead. In this work, we introduce a focused reading approach
to guide the machine reading of biomedical literature towards what literature
should be read to answer a biomedical query as efficiently as possible. We
introduce a family of algorithms for focused reading, including an intuitive,
strong baseline, and a second approach which uses a reinforcement learning (RL)
framework that learns when to explore (widen the search) or exploit (narrow
it). We demonstrate that the RL approach is capable of answering more queries
than the baseline, while being more efficient, i.e., reading fewer documents.

本文介绍了一种专注阅读方法，旨在通过使用强化学习框架（RL）来回答生物医学查询，来提高读取效率。结果表明，相比强基线方法，该方法可以回答更多的问题，同时阅读更少的文献。