Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. While recently released datasets, such as QMSum or AQuaMuSe, facilitate research efforts in QFS, the field lacks a comprehensive study of the broad space of applicable modeling methods. In this paper we conduct a systematic exploration of neural approaches to QFS, considering two general classes of methods: two-stage extractive-abstractive solutions and end-to-end models. Within those categories, we investigate existing methods and present two model extensions that achieve state-of-the-art performance on the QMSum dataset by a margin of up to 3.38 ROUGE-1, 3.72 ROUGE-2, and 3.28 ROUGE-L. Through quantitative experiments we highlight the trade-offs between different model configurations and explore the transfer abilities between summarization tasks. Code and checkpoints are made publicly available: https://github.com/salesforce/query-focused-sum.

本文系统地探讨了神经网络方法在以查询为中心的自动文摘(QFS)中的应用，涉及了两种分类：抽取式-生成式和端到端模型，并提出了两种模型扩展。这些方法在 QMSum 数据集上实现了最先进的性能，并通过人类评估实现了更全面和基于事实的自动文摘。

探索面向查询的摘要的神经模型