  Ranked list truncation is of critical importance in a variety of professional
information retrieval applications such as patent search or legal search. The
goal is to dynamically determine the number of returned documents according to
some user-defined objectives, in order to reach a balance between the overall
utility of the results and user efforts. Existing methods formulate this task
as a sequential decision problem and take some pre-defined loss as a proxy
objective, which suffers from the limitation of local decision and non-direct
optimization. In this work, we propose a global decision based truncation model
named AttnCut, which directly optimizes user-defined objectives for the ranked
list truncation. Specifically, we take the successful transformer architecture
to capture the global dependency within the ranked list for truncation
decision, and employ the reward augmented maximum likelihood (RAML) for direct
optimization. We consider two types of user-defined objectives which are of
practical usage. One is the widely adopted metric such as F1 which acts as a
balanced objective, and the other is the best F1 under some minimal recall
constraint which represents a typical objective in professional search.
Empirical results over the Robust04 and MQ2007 datasets demonstrate the
effectiveness of our approach as compared with the state-of-the-art baselines.
