  Technology Assisted Review (TAR), which aims to reduce the effort required to
screen collections of documents for relevance, is used to develop systematic
reviews of medical evidence and identify documents that must be disclosed in
response to legal proceedings. Stopping methods are algorithms which determine
when to stop screening documents during the TAR process, helping to ensure that
workload is minimised while still achieving a high level of recall. This paper
proposes a novel stopping method based on point processes, which are
statistical models that can be used to represent the occurrence of random
events. The approach uses rate functions to model the occurrence of relevant
documents in the ranking and compares four candidates, including one that has
not previously been used for this purpose (hyperbolic). Evaluation is carried
out using standard datasets (CLEF e-Health, TREC Total Recall, TREC Legal), and
this work is the first to explore stopping method robustness by reporting
performance on a range of rankings of varying effectiveness. Results show that
the proposed method achieves the desired level of recall without requiring an
excessive number of documents to be examined in the majority of cases and also
compares well against multiple alternative approaches.
