# Few-shot Query-Focused Summarization with Prefix-Merging

Source: [Few-shot Query-Focused Summarization with Prefix-Merging](<https://www.sota2.com/research/paper/68fa6e081e209131cea3d89e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:48:10.793Z

Authors: Ruifeng Yuan, Zili Wang, Ziqiang Cao, Wenjie Li

Published: 2022-11-29T12:48:37.000Z

## Abstract

Query-focused summarization has been considered as an important extension for text summarization. It aims to generate a concise highlight for a given query. Different from text summarization, query-focused summarization has long been plagued by the problem of lacking high-quality large-scale datasets. In this paper, we investigate the idea that whether we can integrate and transfer the knowledge of text summarization and question answering to assist the few-shot learning in query-focused summarization. Here, we propose prefix-merging, a prefix-based pretraining strategy for few-shot learning in query-focused summarization. Drawn inspiration from prefix-tuning, we are allowed to integrate the task knowledge from text summarization and question answering into a properly designed prefix and apply the merged prefix to query-focused summarization. With only a small amount of trainable parameters, prefix-merging outperforms fine-tuning on query-focused summarization. We further discuss the influence of different prefix designs and propose a visualized explanation for how prefix-merging works.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2211.16164>)

[PDF](<https://arxiv.org/pdf/2211.16164.pdf>)