🤖 AI Summary
This work addresses the scarcity of query-focused summarization (QFS) datasets by proposing a novel evidence-based method for automatically generating high-quality queries from plain document-summary pairs. By leveraging pretrained language models in conjunction with state-of-the-art QFS systems, the approach effectively bridges the gap between standard summarization data and query-focused settings. Both intrinsic and extrinsic evaluations demonstrate the method’s strong performance: summaries generated using the automatically produced queries achieve ROUGE scores comparable to those obtained with human-written queries, confirming the validity and practical utility of the proposed framework.
📝 Abstract
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.