Few-shot Query-Focused Summarization with Prefix-Merging

• URL: http://arxiv.org/abs/2211.16164v1
• Date: Tue, 29 Nov 2022 12:48:37 GMT
• Title: Few-shot Query-Focused Summarization with Prefix-Merging
• Authors: Ruifeng Yuan, Zili Wang, Ziqiang Cao, Wenjie Li
• Abstract summary: We propose a prefix-based pretraining strategy for few-shot learning in query-focused summarization. With only a small amount of trainable parameters, prefix-merging outperforms fine-tuning on query-focused summarization.
• Score: 10.572282987037353
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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