  Document retrieval enables users to find their required documents accurately
and quickly. To satisfy the requirement of retrieval efficiency, prevalent deep
neural methods adopt a representation-based matching paradigm, which saves
online matching time by pre-storing document representations offline. However,
the above paradigm consumes vast local storage space, especially when storing
the document as word-grained representations. To tackle this, we present TGTR,
a Topic-Grained Text Representation-based Model for document retrieval.
Following the representation-based matching paradigm, TGTR stores the document
representations offline to ensure retrieval efficiency, whereas it
significantly reduces the storage requirements by using novel topicgrained
representations rather than traditional word-grained. Experimental results
demonstrate that compared to word-grained baselines, TGTR is consistently
competitive with them on TREC CAR and MS MARCO in terms of retrieval accuracy,
but it requires less than 1/10 of the storage space required by them. Moreover,
TGTR overwhelmingly surpasses global-grained baselines in terms of retrieval
accuracy.
