从无查询摘要数据自动生成精准查询，提升问答式摘要效果
Generating Query-Focused Summarization Datasets from Query-Free Summarization Datasets
- 基于证据生成模型自动提取查询关键词，无需人工标注
- 生成查询使摘要模型在ROUGE指标上达到与原始查询相当的水平
- 适合需要构建查询聚焦摘要数据集的研究者和应用开发者
大规模摘要数据集广泛用于摘要任务，但通常不包含查询。针对查询聚焦摘要（QFS）任务，本文提出两个核心问题：能否从无查询数据集中自动生成基于证据的查询关键词？基于证据的查询生成是否有助于提升QFS性能？为此，本文提出一种基于证据的查询生成模型。通过对比两个QFS数据集中原始查询与系统生成查询的相似性，评估模型内在质量；同时使用多种预训练模型及SOTA QFS模型进行摘要实验，验证生成查询的外在表现。实验表明，采用证据生成查询所生成的摘要，在ROUGE得分上与使用原始查询的结果具有竞争力。
原文摘要 · Abstract (English)
Large-scale datasets are widely used to perform summarization tasks, but they may not include queries alongside documents and summaries. In the search for suitable datasets for Query-Focused Summarization (QFS), we identify two research questions: Is it possible to automatically generate evidence-based query keywords from query-free datasets? Does evidence-based query generation support the QFS task? This paper proposes an evidence-based model to generate queries from query-free datasets. To evaluate our model intrinsically, we compare the similarity between the original queries and the system-generated queries of two QFS datasets. We also perform summarization tasks using different pre-trained models, as well as a state-of-the-art (SOTA) QFS model, to measure the extrinsic performance of our query generation approach. Experimental results indicate that summaries generated using evidence-based queries achieve competitive ROUGE scores compared to those generated from the original queries.
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