  Query-focused summarization (QFS) aims to provide a summary of a document
that satisfies information need of a given query and is useful in various IR
applications, such as abstractive snippet generation. Current QFS approaches
typically involve injecting additional information, e.g. query-answer relevance
or fine-grained token-level interaction between a query and document, into a
finetuned large language model. However, these approaches often require extra
parameters \& training, and generalize poorly to new dataset distributions. To
mitigate this, we propose leveraging a recently developed constrained
generation model Neurological Decoding (NLD) as an alternative to current QFS
regimes which rely on additional sub-architectures and training. We first
construct lexical constraints by identifying important tokens from the document
using a lightweight gradient attribution model, then subsequently force the
generated summary to satisfy these constraints by directly manipulating the
final vocabulary likelihood. This lightweight approach requires no additional
parameters or finetuning as it utilizes both an off-the-shelf neural retrieval
model to construct the constraints and a standard generative language model to
produce the QFS. We demonstrate the efficacy of this approach on two public QFS
collections achieving near parity with the state-of-the-art model with
substantially reduced complexity.
