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.

本研究提出一种基于约束生成模型 Neurological Decoding 的查询聚焦摘要算法，避免了额外的参数和训练，使用轻量的梯度归因模型从文档中提取重要的标记，并通过操作最终词汇似然使生成的摘要满足这些约束条件。该方法在两个公共 QFS 集合上表现出与最先进模型相近的有效性。